Holographic Document Language: Survey of Related Systems

Overview of the “Holographic” Approach

The holographic document language concept envisions a single declarative specification that simultaneously (1) teaches an LLM the desired document format by example, (2) encodes validation rules (required fields, patterns, etc.), and (3) declares extraction or routing behavior for each piece of data. For example, a line like:

ID:: [ "sess_123" + REQUIRED -> INDEXER ]

serves as a self-documenting schema: it shows an example value ("sess_123"), marks the field as required, and indicates that the ID field should be sent to an INDEXER component. The goal is a single-source, executable spec that is both machine-executable and LLM-legible (understandable by a language model via examples). This section surveys existing systems and standards to see how they compare along the axes of teaching format, validation of structure, and extraction/routing of information. For each, we note what problem it solves, which of the three goals it supports, and where it falls short relative to the holographic ideal.

Declarative Schema Languages

JSON Schema

What it solves: JSON Schema provides a formal grammar for defining the structure and contents of JSON data. It is primarily used to validate JSON documents, ensuring required properties are present and values meet constraints (e.g. number ranges, string patterns) ￼ ￼. JSON Schema is human- and machine-readable and serves as a “blueprint” of what JSON objects are considered valid ￼.

Teach/Validate/Extract: JSON Schema excels at validation – it can enforce types, required fields, value ranges, allowed formats, etc. (e.g. ensuring an age is an integer between 18 and 64 ￼). It also supports basic annotations to aid humans (like "title", "description", or example values in some drafts), which can indirectly help “teach” developers or tools about the expected format. However, these examples and descriptions are not primarily meant for LLM consumption, and JSON Schema does not itself generate examples – it only describes what’s valid. JSON Schema does not natively support extraction or routing instructions; it focuses on describing data shape and semantics “to some extent” ￼ but has no built-in mechanism to trigger actions based on schema content.

Limitations: Relative to the holographic ideal, JSON Schema covers the validation axis but falls short on the integrated teaching and execution aspects. The schema is often separate from the examples – typically, examples are provided in documentation or OpenAPI files rather than the schema itself (though recent JSON Schema versions allow an "examples" array as a metadata keyword). There’s no concept of binding semantic tokens to runtime behavior in the core JSON Schema spec. If you wanted to route or act on data (as holographic spec’s -> INDEXER suggests), you’d need custom extensions or external code. In practice, JSON Schema is often paired with code that consumes validation results or generator tools (for UI forms, etc.), but the spec alone isn’t an executable plan for routing data.

OpenAPI (Swagger)

What it solves: OpenAPI is a standard for describing RESTful API contracts, including endpoints, their parameters, and the structure of request/response bodies. It leverages JSON Schema for data models. The spec is meant to be read by both humans and machines, enabling discovery of service capabilities without needing to read source code ￼. OpenAPI definitions can be used to auto-generate documentation (for humans to learn the API) and code (for servers or client SDKs) ￼.

Teach/Validate/Extract: OpenAPI indirectly supports teaching by including human-friendly descriptions and example values for fields. For instance, an OpenAPI schema for an API response can include "example" or "examples" to illustrate the format. This helps document the format for human developers (and could be shown to an LLM in context as examples). It supports validation in the sense that you can validate actual API requests/responses against the schema (and many frameworks do so at runtime to ensure compliance). OpenAPI also has some notion of semantics – e.g. marking fields as deprecated, enum values, formatting hints – but these are mostly for documentation and client generation rather than enforcement. Regarding extraction/routing, OpenAPI does not directly encode “what to do” with each field; however, it defines operations (endpoints) and ties each data schema to a particular operation. Code generation tools use the spec to create handlers or stubs, which is a form of extraction/execution: e.g. generating a function for an endpoint that yields a data object of the described schema ￼. Still, the actual runtime behavior (what happens to data after validation) is implemented in code outside the spec.

Limitations: OpenAPI is closer to the holographic idea than pure JSON Schema in that it often bundles examples with the schema, effectively “teaching by example” in documentation. But it falls short of a unified executable spec. The spec describes what the API looks like, not how to execute application logic. Semantic or routing annotations (like -> INDEXER) are not standard in OpenAPI, though one could use vendor-specific extensions (x-... fields) to hint at operational intent. In summary, OpenAPI hits validate well (via schema), provides some teach value (examples for humans), but lacks built-in extract beyond generating boilerplate code.

Protocol Buffers (and Apache Avro)

What it solves: Protocol Buffers (protobuf) and Avro are binary serialization formats with schema definitions. They are used to define structured data for efficient storage or RPC, often in distributed systems. You write a schema (.proto files for Protobuf, or a JSON schema for Avro), and tools generate code in various languages to read/write that data. Protobuf is language- and platform-neutral, like JSON but more compact and fast, and it generates native code bindings for ease of use ￼ ￼. Avro similarly defines records, enums, arrays, etc., in a JSON-based schema and emphasizes that data files are always stored with their schema for self-description ￼ ￼.

Teach/Validate/Extract: These systems primarily focus on schema-driven validation and serialization. A protobuf message or Avro record will be validated (by the generated code or library) to ensure it matches the schema – e.g. an integer is present if required, fields have the correct type. Protobuf historically had required/optional labels (proto3 now treats fields as optional by default) and supports default values and basic constraints via field types. Some extensions exist (like Facebook’s protoc-gen-validate) that let you annotate fields with semantic constraints (e.g. “this string must be an email”) which then generate runtime checks – an attempt to bind a bit more semantic validation into the schema. In terms of teaching, protobuf schemas are quite formal and not designed for an LLM to read as examples – they contain field names and types, but no example data inline. Avro schemas, being JSON, could include default values and doc strings, but again no explicit examples of data instances. These formats assume a separate stage (tests or documentation) to illustrate usage. They do support extraction in the sense that once you have a schema, you can automatically parse binary data into structured objects and then use those objects in code. The “operational intent” is typically compiled into the program – e.g. if you have a message Person { name, id, email }, you might have code that, after parsing, stores Person.email in a database. But the schema itself doesn’t contain directives like “-> INDEXER”; the mapping to runtime logic is through generated code and the developer’s integration.

Limitations: Protobuf/Avro excel at shape validation and cross-language data exchange, but are not LLM-legible or example-driven. There is no notion of embedding an example value in a .proto file (beyond comments for humans). The specs are also not inherently “executable” beyond enabling (through codegen) the serialization processes. They don’t unify the documentation of format with its execution. For instance, a proto schema doesn’t tell you what to do with a field – that’s left to service definitions or business logic. In the holographic sense, they cover validate thoroughly, teach minimally, and extract only via the heavy approach of code generation.

(Apache Avro difference: Avro schemas are always included with the data, so a reader can decode data on the fly without prior agreement ￼ ￼. Avro also allows schema evolution (new fields with defaults, etc.), and when using code generation or the Avro library, it will apply defaults and validate types at runtime ￼. But similar to protobuf, the Avro spec doesn’t include execution instructions – it’s focused on ensuring data integrity and interchange.)

Unified Schema and Constraint Languages

CUE Language

What it solves: CUE is a configuration and data constraint language that aims to unify schemas, configuration data, and constraints in one place. In CUE, the same file can contain type definitions, value constraints, and actual data, using a syntax that is a superset of JSON (with enhancements) ￼ ￼. The philosophy is that “types, values, and constraints are all the same” in CUE – there is no hard separation between a schema and an instance ￼. This allows you to define a schema, refine it with additional constraints (like regex patterns, bounds, etc.), and even include concrete example data all in one unified structure ￼. CUE stands for Configure, Unify, Execute, reflecting its goal to be an executable constraint solver for configuration ￼.

Teach/Validate/Extract: CUE supports validation extensively – you can mark fields optional or required, set allowed value ranges, regex patterns, enums, and more. Because it merges schema and data, you can actually embed an example (or default) and have the system verify it. For instance, you might define a schema for a struct and then provide an example instance right below it; running the CUE evaluator can check that the example conforms (or even fill in omitted fields with defaults). This approach means CUE could teach an LLM by example: a CUE spec file often includes example blocks that concretely show the format. The syntax is relatively human-friendly (similar to JSON/YAML but with type notations), so an LLM could potentially read a CUE file and extract the pattern. On extraction/execution: CUE is designed to be executable in the sense that you can run the CUE tool to produce a final JSON/YAML output or validate inputs. It can perform computations like default propagation, and you can write CUE scripts to transform data. However, CUE itself doesn’t trigger external side-effects by default – it’s more about computing a result or verifying constraints rather than calling arbitrary systems. There is work integrating CUE with things like Kubernetes validation or code generation, which hints at binding semantics to action (e.g. using CUE to define cloud resource policies and then feed into an admission controller ￼). Still, in a typical use, if you wanted something like “when field X is present, route to Y”, you would implement that logic outside CUE (or perhaps generate a routing config via CUE).

Limitations: CUE comes very close to the holographic ideal on the teach + validate combination – it literally allows writing the document in the format it defines (the CUE file can contain its own schema and data examples unified ￼). This makes it a strong analog. Where it breaks relative to the holographic approach is binding to runtime behaviors. CUE ensures data consistency but does not inherently know about an INDEXER or other target system – those would be abstract labels unless you write additional code or tooling to interpret them. Another challenge is complexity: CUE’s power (mixing data and constraints, a logical unification engine under the hood) comes with a learning curve. The “executable spec” is not imperative; it’s more like a logic program that must be evaluated. This might be harder for an LLM to fully grok unless simplified to just pattern + comments. In summary, CUE supports validation and partially teaching via examples in one source, but needs an external integration for execution (though it can generate or validate data that then could be used in execution).

JSON-LD with SHACL (Semantic Web schemas)

What it solves: JSON-LD is a way to annotate JSON data with Linked Data semantics – basically, giving context and meaning to JSON fields by mapping them to an ontology or vocabulary (like schema.org). It makes JSON data self-descriptive and unambiguous for machines, using a @context to link keys to IRIs (internationalized identifiers) ￼. SHACL (Shapes Constraint Language) is a W3C standard for describing constraints on RDF graphs (which JSON-LD can be turned into). SHACL lets you define “shapes” that data must conform to – essentially schema rules for graph data, including cardinalities, types, and complex conditions ￼ ￼. For example, SHACL can express that “every Person must have a familyName property” or “the rating property of a Review must be an integer 1–5” ￼ – things that RDF/OWL alone cannot enforce.

Teach/Validate/Extract: JSON-LD + SHACL together address validation and semantics. A JSON-LD context doesn’t enforce structure, but it does teach a machine (or potentially an LLM) what the terms mean. For instance, if your spec says "ID": "schema:identifier" in the context, an LLM that knows schema.org might infer that ID is a unique identifier concept. SHACL shapes then provide the validation rules (e.g. sh:minCount 1 on schema:identifier means it’s required, sh:pattern can enforce a format, etc.). SHACL is very expressive; it can even run custom code (via SPARQL or JavaScript) for complex rules. This covers the validation axis thoroughly (including semantic consistency, not just structure) ￼. However, teaching via SHACL is limited – shapes are machine-oriented RDF graphs themselves, not easily readable examples. You wouldn’t feed raw SHACL turtle syntax to an LLM expecting it to produce valid output (it’s too abstract). JSON-LD examples could be shown to an LLM, which would teach format and also give semantic hints (via context) about meaning – but this is an indirect form of teaching. On extraction/routing, semantic web approaches are more about interoperability than imperative routing. Yet, one could imagine using the semantics to route data: e.g. if data conforms to a RiskLogEntry shape, send it to a risk log store. In practice, using SHACL in execution is usually about rejecting data that doesn’t conform or categorizing data. There isn’t a standard “TARGET -> ACTION” in JSON-LD or SHACL. Nonetheless, a system could use shape validation results to trigger events (e.g. any violation logged, or different shapes sent to different handlers). For example, some pipelines use SHACL shapes to validate incoming data and then load it into appropriate graph databases or indices – a manual but semantically-driven routing.

Limitations: The combination of JSON-LD + SHACL provides a rich semantic schema, but it is quite heavyweight and not designed for ease of use in prompt engineering. The holographic approach emphasizes being LLM-legible and straightforward; by contrast, SHACL is more for software agents and developers with domain knowledge. It lacks inline examples (you would document shapes separately). Also, SHACL isn’t widely used outside specialized semantic web circles, due partly to complexity. It solves a slightly different problem (ensuring data quality in knowledge graphs ￼) rather than instructing an AI assistant how to format output. Thus, while it nails validation (with semantics), it does little for teaching via examples or tying directly to execution beyond the pass/fail of validation. One noteworthy aspect: JSON-LD does allow data to be self-descriptive. In a way, an LLM given a JSON-LD context might better understand what each field represents (which could improve the quality of its output for that field). This is a unique angle (teaching semantics, not just format), but currently we don’t see LLMs explicitly consuming JSON-LD contexts in mainstream usage.

Code-Centric Validation Libraries

Zod (TypeScript)

What it solves: Zod is a TypeScript-first runtime schema validation library. It allows developers to define a schema in code (using a fluent API) and then parse/validate data against it. On success, it returns a strongly-typed object that TypeScript knows matches the schema ￼ ￼. In essence, Zod brings type checking from compile-time to runtime, ensuring that external or untrusted data conforms to the expected shape and types before use.

Teach/Validate/Extract: Zod is primarily about validation (and type inference). You define, for example, const UserSchema = z.object({ name: z.string(), age: z.number().min(0) }); and then UserSchema.parse(data) will throw if data doesn’t match (or coerce if configured). It can enforce constraints like string regex patterns, numeric ranges, enums, etc. Zod schemas can be converted to JSON Schema or used to generate documentation, but typically they don’t carry example data. So Zod alone doesn’t provide an explicit teaching-by-example mechanism; any examples would live in tests or comments. An LLM wouldn’t easily intuit the expected format just from the Zod code (unless it’s very familiar with Zod syntax). However, Zod does integrate with TypeScript, so the act of writing z.object({ ... }) is itself a form of specification that developers (and AI coding assistants) can read. For extraction, once data is validated, you get a native JS object with proper types – at that point, you can route or use it in code as needed. Zod itself doesn’t automate routing, but because it lives in your application code, the “routing” is whatever you code next. For example, after parsing userData with UserSchema, you might call saveUserToDB(userData) – the schema ensured the object is correct for that function.

Limitations: Zod is very developer-centric and not a separate “schema file” that you might present to an LLM. It lacks an integrated notion of semantic actions – it’s just validation. Compared to the holographic approach, Zod covers validation thoroughly, but the teaching is implicit (relying on developer intuition or separate documentation) and execution logic is entirely outside the schema (in the surrounding code). There’s no way to embed in a Zod schema something like “on failure, do X” or “route this field to Y module” – you handle that manually. One interesting point: Zod can generate a JSON Schema or integrate with documentation tools, meaning the single source (the Zod definition in code) can drive other representations. But this is more about avoiding duplication between TypeScript types and validation logic, rather than combining example, validation, and runtime semantics in one artifact.

Pydantic (Python)

What it solves: Pydantic is a popular Python library for data validation and settings management. It allows you to define Python classes (models) with type hints, and it will automatically validate and convert input data to those types. It’s used in frameworks like FastAPI to validate request bodies and query params. Pydantic’s philosophy is “data parsing and validation using Python type annotations” ￼ – you get the ease of Python dataclasses but with runtime checks and type coercion.

Teach/Validate/Extract: Like Zod, Pydantic’s strong suit is validation and transformation. You can mark fields with types (including complex types, nested models, lists, etc.), provide constraints (e.g. Field(..., regex="^abc") for a string pattern), and even write custom validators. Pydantic will ensure, for example, that an email field matches an email regex or that a list of int has no negatives, if you specify those rules. It also can generate a JSON Schema representation of your model – which is how FastAPI produces OpenAPI docs for your APIs. This means any examples or descriptions you attach (via Field(description="...", example=123)) can flow into generated docs. In that sense, Pydantic does support a bit of teaching: you can include an example for a field or an entire example model in docs, which serves as a guide for humans using the API. An LLM could be shown those examples from the documentation. But within the Pydantic class definition itself, the example is just metadata. The primary “example” is usually separate (like an example JSON snippet in OpenAPI docs or tests). On extraction, Pydantic models, once validated, give you a Python object (YourModel instance) that you can then use directly. In frameworks, this is effectively automatic routing: e.g. in FastAPI, you declare an endpoint function that takes a Pydantic model as a parameter, and FastAPI will parse the request JSON into that model (validating on the way) and hand it to your function. That is a clear linkage from spec (the model) to execution (the function logic) – albeit orchestrated by the framework. Pydantic itself doesn’t know about external systems like “INDEXER”, but you might name a field or model in a way that implies its destination.

Limitations: Pydantic, especially V1, had limitations in expressing certain constraints (Pydantic V2 expanded capabilities using pydantic-core). But in comparison to the holographic approach: Pydantic covers validation and is often used in a context that also executes something with the data (like API handling). The spec (model) is close to code and thus not as accessible to direct LLM prompt injection as a standalone schema document. There’s no native concept of including an example and rules and actions all in one place – instead, the model plus its docstring and field metadata cover format and rules, and the actions are in the Python functions that use the model. One could argue that using a Pydantic model in an LLM-powered app (for output parsing) is a step toward holographic: e.g. you generate output from the LLM, feed it into MyModel.parse_obj(), and if it succeeds you proceed to use the data (route it), if not you ask the LLM to fix it. Indeed, libraries like Guardrails and LangChain facilitate exactly this – using Pydantic models as output schemas ￼ ￼. This shows that combining Pydantic (validation) with LLM prompting (perhaps giving it the model schema in words or using function calling) can achieve teach/validate/extract loop, but Pydantic alone doesn’t teach the LLM the format – it’s the glue code around it that does.

Grammars and Executable Specifications

Attribute Grammars and PEGs

What they solve: Parsing expression grammars (PEGs) and related grammar formalisms allow one to formally define the syntax of a language or data format. An attribute grammar extends a context-free grammar with attributes and semantic rules attached to grammar productions ￼. These attributes can propagate information or perform computations as the parse tree is built ￼ ￼. In compiler design, attribute grammars are used to enforce context-sensitive constraints (e.g. type checking rules) and even to generate code from the source code structure ￼. In other words, they turn a purely declarative grammar into an executable specification by attaching code or evaluations to the grammar rules.

Teach/Validate/Extract: A plain grammar (BNF/PEG) by itself is a strict validation tool for structure – it will only parse strings that conform to the grammar, thus enforcing format. That is akin to validation. It doesn’t inherently teach by example; however, a grammar is a form of teaching a machine the language. If an LLM is provided with a PEG for output (or if decoding is constrained by a grammar, as some libraries do), it can guide generation to be valid. But reading a formal grammar might be challenging for an LLM unless it’s been trained on such or we annotate it. Attribute grammars add the extraction aspect: as input is parsed, the attached semantic actions can build an output (like an AST or even direct target code). For instance, in a simple attribute grammar for arithmetic, as it parses an expression, it can compute the expression’s value or produce bytecode ￼. This means the spec is simultaneously validating syntax and executing semantics. In theory, an attribute grammar could be written for a structured text format (like the holographic DSL itself), and it could not only validate that an output is well-formed but also, say, populate a data structure or call certain functions when certain patterns are recognized. That’s very powerful – it covers validate and extract/execute.

In terms of teaching, attribute grammars (or any grammar spec) often come with examples in documentation, but the examples are separate from the grammar specification. The grammar itself is usually not “self-demonstrating” – it’s a set of rules, not a concrete instance. However, one can provide an example string that is expected to parse, as a sort of test case (some grammar formalisms allow embedding test cases). If we consider modern uses: some LLM tooling can take a grammar and restrict the LLM’s output to that grammar. For example, OpenAI’s function calling internally uses a JSON schema which can be seen as a kind of grammar for the function arguments, and other libraries allow providing a CFG or PEG to the decoder to ensure only valid strings are generated. This essentially turns the grammar into a real-time validator and guide, helping the LLM produce only structurally valid output. That addresses teach (by elimination of invalid forms) and validate inherently, but extraction still requires parsing the final output into a structure.

Limitations: The major drawback is that writing attribute grammars or PEGs is a specialized skill. They can be quite verbose and not as intuitive to non-experts. For the holographic goal, we want something easily read/written by humans (perhaps domain experts) and LLMs. A raw grammar is hard for humans to read as an example of the format – it’s abstract. Attribute grammars also can become complex; ensuring that the semantic rules don’t have circular dependencies or that the grammar can parse all intended inputs can be non-trivial. Another limitation is error-handling – a grammar will typically reject malformed input, but an LLM might produce almost valid text that fails in subtle ways that are hard to debug from the grammar alone. In practice, while attribute grammars allow executable specs (validation + semantics in one), they have commonly encountered failure modes: if used for generation, they can be too restrictive (the model might get stuck or produce nothing if the grammar and prompt aren’t perfectly aligned), and if used for validation only, they often produce poor error messages (hard to pinpoint what went wrong, which is tough if you want to ask an LLM to correct itself). Historically, in software engineering, attempts at executable formal specs often run into maintainability issues – the spec can become as complex as code, defeating the purpose. This is why simpler schema languages (JSON Schema, etc.) that don’t try to solve semantics are more popular: they draw a clear line between structure and behavior. The holographic approach aspires to cross that line, so it must learn from how attribute grammars pack too much into one construct.

Literate and Behavior-Driven Specifications (e.g. Gherkin/Cucumber)

What they solve: Another angle on executable specification is Behavior-Driven Development (BDD) tools like Cucumber, which use a semi-structured natural language format (Gherkin syntax: “Given… When… Then…”) to specify behavior examples. These scenarios read like plain English documentation of system behavior and are linked to code (“step definitions”) that execute the described actions. This isn’t a schema for data format, but it is a unified way to express an example and a validation in one. For instance, a Gherkin scenario might say:

Given a user with ID "sess_123" exists
When the user requests their profile
Then the response should include "status": "ACTIVE"

This is both documentation (teaching a human what should happen) and a test (which will fail if the code doesn’t produce the expected output).

Teach/Validate/Extract: BDD specs teach by example – they are literally examples of system usage in a readable form. They validate because they are run as tests (if the system’s behavior deviates, the test fails). And in terms of extraction/execution, the link is through the step definition code that parses each step sentence and calls into the system (or verifies outputs). The scenario itself doesn’t contain code, but it routes to code via matching patterns. This parallels the holographic idea: the spec says something like ID must be in this format (example) and goes to INDEXER, and one could imagine a step definition “goes to INDEXER” that is bound to a function send_to_indexer(field, value).

Limitations: The BDD approach is domain-specific to testing and is less formal for data schemas. It’s verbose (natural language) and not meant for LLM output formatting per se (though one could use LLMs to generate Gherkin scenarios!). The structure is also not as compact as the holographic DSL line (which packs example + rule + target in one line). But the key takeaway is that mixing human-readable examples with machine-executable hooks is a proven concept (e.g., Gherkin + Cucumber), albeit in a different domain. A failure mode here is ambiguity – natural language steps can be interpreted incorrectly by humans or mismatched to code if not carefully written. The holographic approach would avoid natural language ambiguity by having a more formal syntax, but it should strive to remain readable to non-programmers to succeed.

In summary, grammar-based and literate spec approaches show that it’s possible to have a single artifact both describe and drive behavior. The trade-off is often complexity vs. clarity. We will see that many combined approaches become hard to manage, which is why narrower schemas or simpler prompt schemas are more popular for now.

LLM-Centric Structured Output Tools

OpenAI Function Calling (API “functions” parameter)

What it solves: Function calling is a feature in the OpenAI API where the developer provides the model with a list of function signatures (name, description, and JSON-schema-like parameters for each function). The model can then decide to output a JSON object indicating a function call with arguments, instead of just free text. This allows a structured interface between the LLM and external tools: the model effectively formats its output as a JSON conforming to your schema, which the client code can parse and call the corresponding function ￼ ￼. It’s a way to get guaranteed well-structured output (the model is constrained to produce valid JSON for function args) and to integrate with code.

Teach/Validate/Extract: Function calling hits all three goals in a limited but effective way: The function schema teaches the model what format and content you expect by providing types, allowed values (enums), and descriptions for each field. The model sees, for example, that there is a function extract_student_info with parameters like name: string, grades: integer and a description for each. While no concrete example is given, the model’s training and the schema guide it to produce, say, {"name": "David Nguyen", "grades": 3.8, ...} for a student info extraction ￼ ￼. The schema serves as both instruction and constraint. On validation, the OpenAI system itself validates that the model’s output can be parsed as JSON and matches the schema (they have “JSON mode” to ensure the output is valid JSON ￼). The developer can then double-check types (e.g., confirm grades is an integer if needed). This drastically reduces format errors – as one tutorial noted, the model will return consistent output (e.g. GPA as a number not a string) when using function schemas ￼. For extraction/routing, the mechanism is built-in: the model’s structured output explicitly tells the application which function to call and with what data ￼. So if the model outputs {"function": "logDecision", "arguments": {...}}, the client knows to call logDecision with those args. This is essentially routing the content to a specific codepath (the function implementation). It’s one-step: model output -> function call -> you handle the rest. In effect, the specification of the function in the prompt binds the output to execution (the code you wrote for that function).

Limitations: Function calling is a narrower take on the holographic idea, focused on JSON outputs for tool use. It doesn’t allow arbitrary inline examples in the spec – you can’t show a full example in the function definition beyond maybe putting one in the description text. The model has to infer the style from the schema. So the teaching is weaker than a literal example, but still effective given the model’s training on code and schemas. The validation is primarily structural; semantic correctness still relies on either the model’s understanding or post-processing (the model might fill an argument with something plausible but incorrect factually, and the system wouldn’t catch that unless additional validators are in place). For extraction, it’s excellent at hooking to runtime, but it’s limited to function calls – it doesn’t cover scenarios where you might want the model to produce a document rather than trigger a function (although you can combine approaches). Also, the function calling feature is specific to certain model endpoints and has a token cost (you’re stuffing the schema in the prompt context). For complex schemas, this can become unwieldy. In comparison to a hypothetical holographic DSL, OpenAI’s approach is more implicit: the schema is separate from any example documents, and it’s not meant to be read by humans as documentation (though it’s readable, it’s JSON after all). It’s largely a machine-facing spec. The holographic approach as described by the user values LLM-legibility (and presumably human legibility), which might mean a more text-friendly format than raw JSON.

In practice, function calling has proven a powerful way to get structured JSON out of GPT models, and it shows that models can follow a spec that combines structural directives with operational intent (call this function) fairly reliably. It’s arguably the closest current technique to achieving a combined validate+execute spec for LLMs. The breakdown is that it doesn’t intrinsically provide example-based teaching (no few-shot examples by default) and it is constrained (you must predefine all possible function schemas).

Guidance (Microsoft’s Guidance Library)

What it solves: Guidance is an open-source library that provides a programming model for templating LLM prompts and controlling the generation. With Guidance, you write a prompt with special syntax to intermix constraints and model generation. For example, you can write a template that tells the model to fill in a JSON structure, or ensure a certain format, or even do looped calls. Guidance can enforce that certain parts of the output match a regex or a type by validating tokens as they stream and steering the model when it goes off course. Essentially, it’s a form of dynamic prompt with execution, bridging between pure prompting and writing code. The Microsoft Research page describes it as letting developers “express in Python the precise programmatic constraints the model must follow for structured output” and achieving “100% guaranteed output structure” by steering the model token-by-token ￼ ￼.

Teach/Validate/Extract: In Guidance, you often include an example or schema right in the prompt template, which is directly shown to the model – this is strong teaching. For instance, you might create a prompt: {"ID": "{{gen 'id' pattern='[A-Za-z0-9]+'}}", "STATUS": "{{select 'status' from=['ACTIVE','INACTIVE']}}", ... } which demonstrates the JSON format and uses the library to ensure the id generated matches a pattern and status is one of given choices. The model, guided by the template, learns exactly the required format from the prompt itself. The library then validates each generated token against the constraints (regex, allowed set, type length, etc.) in real-time ￼. If a token would violate a constraint, Guidance can stop and retry or adjust the probability distribution to avoid invalid tokens. This effectively guarantees the output conforms to the spec, eliminating the need for after-the-fact parsing fixes – it’s validation during generation (the “token by token steering”). After generation, Guidance allows you to directly extract the captured pieces (like id, status variables from above) because you set them in the template. So you get a Python dictionary or object with those fields, ready for use. This covers the extraction goal neatly – the output is already structured in a data object as well as a string. If you want to route parts of it, you could write your template to call different generation functions or handle subparts separately. In essence, Guidance provides an execution layer for the spec: the spec lives partly in the prompt (as a template with logic), and the library’s runtime enforces and executes it.

Limitations: Guidance is very powerful but requires the user to design a prompt program (which might include control flow, error handling, etc.). The “spec” is not a static artifact like a schema file; it’s entwined with the prompting code. This means it’s less declarative and perhaps less easily shared as documentation, compared to say a standalone holographic spec file. It’s more like writing a small program to get structured output, rather than declaring a schema that is inherently understood by the model. Also, maintaining these templates for large schemas can become complicated. The approach is highly tied to controlling the model’s decoding, which might make it model-specific at times (though Guidance works with many models). Another consideration: because Guidance guarantees structure, it might sometimes guide the model too rigidly, potentially obscuring errors (you might get a syntactically perfect JSON that is semantically nonsensical, and Guidance would not catch that unless you add semantic checks or validations on content). So similar to function calling, it assures form but not content truth. Relative to the holographic approach, Guidance exemplifies executable spec – the spec literally executes as the prompt is processed. The holographic DSL might be aiming for something a bit more static (write once, use for both prompting and validating). Guidance requires you to run its system; a holographic spec might be convertible to a Guidance template or to other forms. In fact, a plausible implementation of a holographic spec language could be to compile it into a Guidance program (for generation) plus a JSON Schema or code (for validation and routing).

Outlines (Outlines.ai Library)

What it solves: Outlines is another library focused on structured LLM outputs. It provides a high-level interface where you can simply specify the desired output format using Python type hints or Pydantic models, and it will ensure the LLM produces output of that shape ￼ ￼. Outlines supports things like providing a Literal["Yes","No"] to constrain output to one of those strings, or giving a Pydantic BaseModel to have the model fill out a complex object ￼ ￼. Under the hood, Outlines uses techniques like automatically constructing grammars/regex for the model or sampling in a way that guarantees the format (the library’s docs boast “no more broken JSON – guaranteed valid structure” ￼).

Teach/Validate/Extract: Using Outlines, the teaching happens by the library injecting the appropriate guidance to the model. For example, if you say output = model(prompt, output_type=MyPydanticModel), Outlines will behind the scenes prompt the model in a way that enumerates the fields of MyPydanticModel and their types, possibly with placeholders, effectively showing the model what is expected. The developer doesn’t directly craft the prompt; the library does it based on the schema provided, which lowers effort and errors. On validation, Outlines guarantees the structure by constraining the decoding process (similar to Guidance, it can use a regex or a context-free grammar derived from the type spec to filter tokens ￼ ￼). After generation, it will also parse the output into the Python object (e.g., using Pydantic’s parser), so you end up with a validated object or an error if somehow it didn’t match. In many cases, the parsing is done progressively, ensuring no invalid JSON syntax, etc. Because Outlines works “during” generation, it rarely has to fall back to heavy retry logic; it tries to keep the model on track (the creators highlight performance: structured generation is almost as fast as free text generation with their method ￼). As for extraction, since you directly get a Python object (like an instance of your Pydantic model or a simple int/str), you have effectively extracted the data. Routing then is trivial – you might pass that object to another function. For example, in an e-commerce categorization example, Outlines could directly yield a ProductCategory object that your code then uses to route the item to the proper category handler.

Limitations: Outlines, like Guidance, abstracts a lot of complexity. The “spec” here might just be a Python type definition or Pydantic class. While that’s convenient, it’s not a standalone DSL or document – it means your specification is embedded in code. This is great for developer productivity but not ideal if you wanted, say, a non-programmer to author or read the schema. In the holographic concept, one imagines a single document possibly edited by non-developers to configure the system. Outlines still assumes a developer defines types in Python. Another limitation is that Outlines currently supports types that can be expressed as Python type hints or simple regex; very complex interdependencies might be hard to enforce (though Pydantic validators could be involved). Also, because it’s high-level, if something goes wrong (like model output doesn’t match the shape), the developer might have less insight than if they wrote their own prompt template – but the library likely provides errors or re-prompts automatically. Overall, Outlines demonstrates that a schema-to-generation pipeline can be user-friendly: you declare the structure, the library takes care of teaching the model and validating the output. This is very much aligned with the holographic vision, except that the “spec” in Outlines is not necessarily LLM-legible examples but rather code-driven schema. Perhaps an evolution could allow writing the schema in a YAML/DSL that Outlines consumes, making it more accessible – which is essentially what Guardrails does (see below).

Guardrails (RAIL spec)

What it solves: Guardrails AI is an open-source framework specifically designed to validate and correct LLM outputs according to a schema or set of rules. It introduces the concept of a RAIL (Reliable AI Language) spec, often written in YAML or XML, where you define the expected output schema (JSON/XML), types, allowed values, and even constraints like “this field should be a valid email” or “avoid profanity in this field”. Guardrails then monitors the LLM’s output: if the output doesn’t conform, it can automatically retry, ask the model to fix it, or apply some fix-up rules. It’s aimed at making LLM outputs safe and reliable, covering structure and content checks ￼ ￼.

Teach/Validate/Extract: In Guardrails, the teaching is achieved by compiling the output schema into the prompt. The framework will include instructions to the LLM about the required format (for instance, it might prepend a system message that describes the output schema in words or provides an example, or it uses few-shot where the assistant output is in the desired format). Guardrails essentially acts like a safety net: it gives the model a blueprint, but if the model deviates, Guardrails catches it. The spec itself can contain example outputs as part of documentation (the YAML supports a format definition section), so it’s quite legible. For validation, Guardrails uses the spec to check the model’s output. If you said a field is an integer or matches a regex or is within a range, it will validate that after generation ￼ ￼. It supports built-in validators (regex, length, JSON Schema types, even semantic checks via classifiers) ￼. On failure, it can either repair (e.g. run a transformation to fix quotes in a JSON) or retry by re-prompting with feedback. This gives a robust validation loop rather than a single-shot. Regarding extraction/routing, once the output passes validation, you have it parsed as a Python dict or Pydantic model (Guardrails can integrate with Pydantic too, as shown in the snippet where Guard.from_pydantic(output_class=YourPydanticClass) is used ￼). So getting the structured data out is straightforward. If the spec had multiple sub-outputs (e.g. the user asked for both a summary and a JSON, etc.), Guardrails can manage that too by having sections in the spec. Routing in Guardrails can also mean choosing different “rail” flows based on input or output (though that’s more of an advanced use where you have conditional flows in the spec).

Limitations: Guardrails is somewhat “heavyweight” in that you need to maintain a separate YAML spec file alongside your code. This spec is quite close in spirit to the holographic concept: it’s a single source of truth for output format and some semantic rules. However, the YAML syntax can be verbose, especially for nested JSON, and it may not be as immediately intuitive as a simple bespoke DSL line like the ID:: ["sess_123" + REQUIRED -> INDEXER] example. It’s also primarily focused on after-the-fact validation and correction. It does guide the model with instructions, but it doesn’t, for example, do token-level steering like Guidance. So there is a chance the model produces an invalid output and only then it gets caught and fixed (with a re-prompt). This can increase latency (multiple calls) compared to approaches that get it right first time. From a holographic standpoint, Guardrails covers validate comprehensively and attaches an “operational intent” of sorts by allowing you to specify what to do on failure (retry, etc.), but it doesn’t inherently connect fields to arbitrary code actions beyond validation. You still have to interpret the final data and route it in your code. The “-> INDEXER” concept would be outside Guardrails’ scope; you’d use the output object to decide calling an indexer. That said, nothing stops one from writing a post-process that reads the spec or uses field names to route logically. It’s just not declared in the spec in a first-class way.

One common challenge (not unique to Guardrails): If the model is way off (like it misinterprets the request or refuses), the spec might catch structural errors but the content might still be useless (garbage in valid format). Guardrails can apply semantic validators (e.g., check that an answer actually addresses a question via an entailment model), but those have to be specified explicitly. This underscores that validation of format is easier than validation of intent or correctness – a gap outside the scope of most schema-like systems.

Comparative Coverage: Teach vs Validate vs Extract

To summarize the systems surveyed, we can map each to the three goals:
	•	JSON Schema: Primarily Validate (structure and basic semantics) ￼. Minor Teach via examples/descriptions for humans, no direct Extract (requires external code). Breaks down on tying to runtime behaviors (no execution semantics).
	•	OpenAPI: Validate (schema for APIs) and some Teach (examples in documentation, informs humans and possibly fine-tuned models) ￼. Codegen provides a form of Extract (generating stubs), but the spec itself isn’t an execution plan. No in-line semantics beyond data shape.
	•	Protobuf/Avro: Strong Validate (via type enforcement in code) ￼. Teach only for developers (through comments or usage examples outside the .proto). Extract via generated code objects, which is a compile-time binding rather than runtime decision encoded in schema. No live semantics (aside from optional custom validate annotations).
	•	CUE: High Validate (rich constraints, types, defaults unified with data) ￼. Medium Teach (can include example data right in spec, which demonstrates format) ￼. Partial Extract (CUE can compute and emit final configs; one could interpret target labels from CUE, but it doesn’t inherently call external services). No built-in side-effect mechanism, but could be integrated with other tools.
	•	Zod/Pydantic: Strong Validate (runtime type checking, constraints in code) ￼ ￼. Little direct Teach (mostly separate from spec, though Pydantic can supply example for docs). Extract in the sense of returning a typed object for use in code (developer does the routing). They are developer-oriented; not meant to convey format to an end-user or LLM except via generated docs.
	•	JSON-LD+SHACL: Validate (very strong for RDF graph constraints) ￼. Teach semantics (via linked data context, gives meaning to fields) but not format examples. Extract not explicit – you can validate and then manually route or use data based on ontologies (e.g., if an object is a RiskLogEntry type, send to risk log). Essentially, semantics could drive routing, but outside the spec.
	•	Attribute Grammars/PEG: Validate (grammar enforces structure by parsing) ￼. Teach minimal (needs separate examples or prompt engineering to use grammar in generation). Extract possible (attribute rules can produce output or trigger code) ￼. In theory, covers all three, but with high complexity and not user-friendly.
	•	BDD Specs (Gherkin): Teach (scenarios are concrete examples in plain language). Validate (they serve as tests – pass/fail indicates spec adherence). Extract (step definitions execute the described actions). However, they are limited to describing interactive behaviors, not arbitrary data schema, and require a lot of human interpretation/maintenance.
	•	OpenAI Function Calling: Teach (schema informs model of expected JSON keys/types) ￼, though no explicit example given. Validate (model constrained to output parseable JSON; developer and OpenAI system validate it matches schema) ￼. Extract (direct: model outputs a function name and args, which the system then calls – routing is built-in) ￼. Breaks down if format needs are more free-form (it’s basically JSON only, no long textual outputs with embedded data, etc.), and it doesn’t show the model a full example output.
	•	Guidance: Teach (prompt template literally walks the model through the format, possibly with partial examples or explicit token expectations). Validate (token-level enforcement of structure/regex/choices) ￼. Extract (captures output into variables/objects as it’s generated). The “spec” here is an executable prompt program rather than a separate schema file. Powerful but requires coding the logic – not a static one-liner specification.
	•	Outlines: Teach (automatically constructs appropriate prompt or decoding constraints from a given type/schema) ￼. Validate (guarantees well-formed output matching the schema, using grammars or constrained decoding) ￼. Extract (returns data in structured form, e.g., a Pydantic model instance or Python native type). Abstracts away the prompt engineering; spec is implicit in the Python type system. Could be limiting for non-Python use cases or as a shareable artifact.
	•	Guardrails: Teach (prepends/augments prompts with format instructions derived from the RAIL spec; can even include few-shot examples in the spec for the model to follow). Validate (after generation, applies schema and custom rule checks, with auto-correction) ￼ ￼. Extract (outputs parsed data if valid). Even allows some execution logic on failure (like retry or call a fallback). Does not itself call external functions as part of spec (other than validators), so routing of successful output is up to your code.

We see that no single existing system perfectly ticks all boxes in a simple, unified way. There is a gap that the holographic document language is attempting to fill by being simultaneously didactic (for the LLM), prescriptive (for validation), and operative (for driving execution). Some systems come very close in spirit (Guardrails, Outlines, function calling) but each has its focus and assumptions.

Closest Analogs, Novelty, and Failure Modes

Bringing these comparisons together, what comes closest to the holographic idea? Arguably, Guardrails’ YAML RAIL and Outlines’ structured output types are nearest in the LLM domain. Guardrails lets you define a format with examples and validators in one file – a bit like a holographic spec (though not quite in the “document is written in the format it defines” style; it’s more a meta-spec). Outlines takes a developer-friendly schema and essentially enforces it at generation time, which is the execution of a combined spec (but the spec itself is implicit in code). Also notable is CUE in the non-LLM space, as it unifies examples with rules, fulfilling the “self-contained spec” idea to a large extent ￼. The OpenAI function calling + JSON Schema approach is another analog: it pairs a format spec with direct execution (function invocation) ￼. If we consider outside the LLM world, attribute grammars provided a way to mix spec and execution (though not in a user-friendly, LLM-legible form) ￼, and behavior-driven specs (Gherkin) mixed documentation with automated execution via step bindings. Each of these addresses parts of the trifecta:
	•	Schema+Execution: Attribute grammars (hard to read), function calling (but minimal teaching), BDD (but for scenarios, not data schemas).
	•	Schema+Example: CUE (data with schema), OpenAPI (schema with example, but no execution binding).
	•	Example+Execution: BDD again (example scenario triggers code), or even documentation with embedded tests.

What is genuinely novel? The holographic approach specifically emphasizes a single syntax that is simultaneously an example (showing the shape), a validation rule, and a routing directive. This is a kind of literate, self-descriptive schema. The novelty is in balancing these three aspects in one notation that is legible to an LLM. Many existing solutions pick two of the three: for instance, JSON Schema + function calling covers validate+extract well but doesn’t embed examples for teaching; Guardrails covers teach+validate but stops short of automatic extract to multiple endpoints (beyond giving you the final object); CUE covers teach+validate in a unified way, but not routing actions. The holographic idea also leans towards a document that is written in the format it defines, which suggests a recursive, self-exemplifying structure (the provided example file did exactly that, defining the DSL using the DSL itself). This self-referential bootstrapping is reminiscent of how some language compilers are written in their own language – it can be powerful for clarity and dogfooding. Not many schema languages do that – you wouldn’t write a JSON Schema in JSON format to describe JSON Schema itself (though technically you could, it’s just very meta and not instructive). Here, the approach explicitly uses examples as part of the rule, not separate. That integration is novel.

Another novel aspect is focusing on LLM legibility. Traditional schemas prioritize machine validation or human developers, not AI comprehension. Designing the spec language so that an AI can easily learn from it (e.g., using natural-ish notation, maybe limited vocabulary, etc.) is a new angle. For example, the holographic syntax KEY:: [ "example" + REQ -> TARGET ] is something an LLM can likely parse: it sees a key, an example in quotes, a tag REQ, and an arrow indicating a destination. This mixes natural language (“required”) with symbolic structure. It’s more readable than a pure JSON or XML schema to the untrained eye, and likely more in line with how LLMs have seen data in their corpus (it almost looks like Markdown or config). So, the novelty is in synthesis: combining proven ideas (examples in spec, formal constraints, runtime bindings) into one coherent language optimized for use with LLMs.

Now, what about common failure modes of similar efforts, and by extension, risks for the holographic approach? A few come to light:
	•	Complexity vs. Expressiveness: Many attempts to unify specification and execution end up either too simplistic (can’t express all the needed logic) or too complex (essentially a programming language, with all the baggage). For example, attribute grammars are powerful but not widely adopted because they require understanding of both parsing and programming in a constrained form. If the holographic language tries to encode very elaborate logic (like conditional routing: “if X is present and Y > 5 send to A else B”), it might become as hard as code. A key is to keep the spec declarative and fairly high-level. The provided example indicates a limited, clear set of constraint keywords (REQ, OPT, REGEX, etc.) and simple target labels – that’s promising. But the more features creep in (calculations, conditionals, loops), the more it risks becoming another general-purpose DSL that few people can read or maintain. The lesson from others is to aim for the 80% use-case and not the 100% if it complicates things drastically.
	•	Spec Drift and Maintenance: One reason API specs and code often drift is that maintaining them in parallel is hard. A single-source spec would solve drift by being the truth for both validation and execution. However, if the spec is not directly usable (i.e., if one still has to write code to enforce parts of it or to act on it), then you might end up updating one without updating the other. Systems like OpenAPI mitigate drift by codegen or by having the spec generated from code. With the holographic approach, if the spec directly drives execution (e.g., the system reads the -> INDEXER target and automatically calls the indexer), then maintaining that binding is crucial. If an indexer API changes, you must update the spec. That’s fine – it’s intended. But if a developer adds a new field and forgets to update the spec, now your LLM might not produce it or your validator might reject it – so developers need to treat the spec as code. This is a cultural shift; as seen with things like JSON Schema or CUE, not everyone is disciplined to update schemas unless it’s made part of the development workflow (some failure modes: schemas become outdated, or teams avoid strict schemas because they slow iteration).
	•	LLM Misinterpretation: While an LLM can be guided by a spec, it might also misunderstand it if the spec is ambiguous or too terse. For example, if a constraint or target name is unclear, the LLM might ignore it or do something odd. The spec needs to be written in a way that the LLM’s likely interpretation aligns with the actual intent. This might mean including explanatory text (like the example had comments “Teaches expected format” next to example, etc.). If the holographic DSL is too symbolic, the LLM might not follow (especially if it hasn’t seen much of it in training). So one failure mode is that the model just doesn’t obey the spec fully, requiring iterative prompt engineering or fine-tuning. The solution could be providing a few-shot in the system prompt: e.g., include the spec of the spec (meta-spec) in the model’s instructions (which is meta but the example does exactly that by having a PATTERN section that shows how to interpret the syntax). This is doable but complex to get right for all models.
	•	Over-Reliance on Model Obedience: Systems like function calling and Guardrails show that models, especially GPT-4 class, generally follow structural instructions, but not 100%. There are edge cases where the model produces something that technically fits the schema but is logically wrong, or tries to be helpful in the wrong way (like adding extra text around a JSON when not needed). We have to consider how the holographic system handles such cases. Likely, it would need a runtime validator (like the Guardrails approach) anyway – meaning even with a perfect spec, you need a process to catch deviations. Therefore, the execution layer can’t assume the model always outputs perfectly; it should validate and possibly loop back if not. That adds complexity (the spec might need to include fallback instructions like “if output invalid, ask the user for correction” or something). If not designed, this is a failure mode others encountered: e.g., naive usage of an LLM with just a spec but no verification can lead to silent failures or messy outputs.
	•	Performance and Scalability: If the spec grows large (imagine a complex document with dozens of fields, each with constraints and different targets), feeding that into the LLM prompt could consume a lot of tokens. This can slow generation and increase cost. The system might need ways to selectively apply parts of the spec depending on context (like active schema sections relevant to a query). That’s a challenge faced in e.g. large OpenAPI specs – you can’t always stuff the entire API description into GPT’s prompt. Approaches like Toolformer (which selects functions) or summary-of-spec might be needed. A minimal execution layer might handle this by dividing the problem: for example, have the LLM generate a draft, then use the spec to parse and validate it outside the model (like a compiler), then only involve the model again if errors. This two-pass approach (generation, then validation) is what Guardrails does, but it can be optimized. Alternatively, one can fine-tune an LLM to internalize the format, reducing reliance on large prompts – but that’s another path (and it means the spec is used in training, which is a different execution layer design).
	•	User Adoption and Wheel Reinvention: One practical risk – if this holographic language is too similar to existing tools, developers might ask “Why not just use JSON Schema + some conventions, or use CUE, etc.?” If it doesn’t provide a clear benefit, it could be seen as reinventing the wheel. However, if it indeed synthesizes the best of each (like JSON Schema’s expressiveness, CUE’s unified data, Guardrails’ repair, Outlines’ ease, etc.) and presents it in a coherent package, it can fill a gap. The execution layer minimal viable product could even build on existing components: for example, under the hood, translate the holographic spec to a JSON Schema for validation (many libraries exist for that), and to a set of function call definitions or a constrained prompt for generation, and to a simple routing config for execution. This way it leverages proven wheels instead of starting from scratch. The novelty is in orchestrating them via one source.

Common failure modes in others to avoid:
	•	When schema and code are separate, they diverge (hence single-source is desired).
	•	When schema is too limited, people put logic elsewhere (so allow some semantic annotation, but carefully).
	•	When schema is too complex, people abandon it (so keep language simple; possibly limit Turing-completeness to avoid writing full programs in spec).
	•	When relying on AI, weird outputs happen (so have robust validation and fallback).
	•	Performance: as noted, ensure it’s efficient (maybe compile to efficient validators, don’t always need the model in loop).

Conclusion: Reinventing the Wheel or Meaningful Gap?

Given the survey, the holographic document language appears to be more of a meaningful synthesis of existing ideas than a wholesale reinvention. It’s assembling pieces that have existed in isolation: the declarative rigor of schemas, the pedagogical value of examples, and the binding of spec to execution seen in some DSLs and modern LLM tooling. No existing single system we examined provides a one-stop solution that a non-engineer could read to understand the format, an LLM could reliably use to generate output, and a machine could execute against for routing logic. There is a gap in the market for such a unified, LLM-aware schema language.

That said, it does reinvent aspects of various wheels – for instance, the validation constraints (required, regex, enum) overlap with JSON Schema, CUE, SHACL, etc.; the routing tags overlap conceptually with function names or destinations in traditional ETL pipelines; the example-as-spec idea has echoes in things like Swagger examples or literate tests. The value comes if the integration is seamless and user-friendly. If not done carefully, one could ask “why not just use JSON Schema and Guardrails together?” – which might achieve similar ends with existing tech. The holographic approach seems to push towards a single source that could generate those, which might indeed be how it’s realized (the holographic spec could compile down to JSON Schema for validation, a prompt snippet or OpenAI function spec for LLM, and some config for routing). In that sense, it’s not overthrowing prior art but standing on their shoulders.

Minimal execution layer required: To make this real, the smallest thing needed would likely include:
	1.	A parser for the holographic DSL – to read lines like ID:: [ "sess_123" + REQ -> INDEXER ] and understand the pieces (field name, example, constraint type, target). This is straightforward string parsing or could even be done with a simple PEG (since the syntax is regular).
	2.	Validation engine: Once parsed, the spec can be converted into validation rules. One could map REQ to “required field in JSON Schema,” REGEX(pattern) to a regex check, ENUM[A,B] to an allowed set, etc. The minimal approach is to compile these to an existing validator: e.g., generate a JSON Schema or a Pydantic model, or simply use the spec directly in code to validate (iterate over fields in output and apply each rule). The spec might itself be used for validation if we choose – for example, since it contains an example of the expected type (like "sess_123" is an example string), the engine knows the type is string. Constraint keywords give more info. Implementing a validator from scratch is doable given limited keywords, but leveraging a library might be faster (e.g., translate to Draft-07 JSON Schema and use a library). The key is handling “unknown fields” (the example spec had a policy for unknown fields: warn or reject), which is also a toggle in JSON Schema (additionalProperties). So minimal execution layer handles that too.
	3.	LLM prompting mechanism: To “teach” the LLM, the execution layer should feed the model the spec or a derivative of it. The naive way: include the spec text itself in a system or few-shot prompt, telling the model “Follow this format.” If the spec is written in the same format it defines, the model seeing it might generalize “I should output data lines like those examples”. However, the model might need a more explicit nudge. The minimal approach could be to include one or two example outputs (perhaps the DATA_FORMAT.EXAMPLE section like in the provided file ￼) as a few-shot example. In other words, the spec is already a demonstration, but the system prompt might say: “You will output a document with the following fields. Here’s the specification:” followed by the spec. With GPT-4, this might suffice. For more control, one could use function calling: transform the holographic spec to an OpenAI function schema so the model naturally outputs JSON. But if the desired output is a text document in that DSL (with KEY:: value lines), perhaps just providing the spec is enough for a capable model. We might still incorporate a checking step: after the model responds, run the validator. If it fails, either send feedback (“Your output didn’t match the required format because… please fix it”) or use a system like Guidance to enforce during generation. The minimal system can start with a simple validate-after approach for simplicity.
	4.	Routing logic: The spec’s -> TARGET directives imply that after generating or receiving a document, the system should take certain fields and send them to certain destinations. A minimal implementation could just be a mapping: e.g., INDEXER means “send to search index”, which could be represented by a function or API call that the execution layer knows. The spec likely has a section defining targets (like in the example, TARGETS:: [META, SELF, INDEXER, ...] ￼). The minimal layer can have a configuration mapping those target labels to actual handlers or endpoints. For demonstration, it could simply print or log: “INDEXER <- ID: sess_123” to simulate routing. In a real system, you’d plug in the actual code (perhaps via a plugin architecture: e.g., if target == INDEXER, call IndexerClient.index(field, value)). The key point is that the spec drives this: if a field is marked for INDEXER, we don’t need further hardcoding of field name in code – we can loop through the parsed output and dispatch by target. This achieves the “single source of truth” for what goes where.

In essence, the minimal execution layer is a spec compiler + orchestrator: it takes the holographic spec, produces a prompt template or instructions for the LLM (teach), validates the LLM output (validate), and invokes callbacks for each target on the data (extract/execute). Each of these could initially leverage existing tech (e.g., use Python’s re for regex checks, use OpenAI function calling or a regex filter for structured output, etc.) to avoid reinventing algorithms.

Is it reinventing the wheel? It’s more like combining wheels into a car. Many of the components exist (wheels, engine, chassis = schema, examples, execution hooks), but the holographic document language is trying to be the car that moves forward on its own. If successful, it would represent a meaningful gap filler: a developer or analyst could write one document and get the benefits of documentation, validation, and automation from it. Given the continued rise of LLM applications, having such a single-source schema for prompt and post-processing is quite appealing and not fully solved by existing standards.

The challenge is to design it so that it’s not too burdensome to adopt (it should feel like an aid, not another complex language to learn), and to implement it in a robust way (leveraging lessons from those analogous systems to avoid their pitfalls). If that can be done, this approach indeed addresses a gap – enabling high-level specs that both guide AI generation and drive program logic, which could streamline development of reliable LLM-driven applications.
