Comparative 5 min read · May 2026

# Mneme HQ vs RAG-based Governance

RAG retrieves relevant context at query time. Mneme HQ enforces architectural constraints at generation time. These are different layers of the same problem — and confusing them is the most common governance mistake teams make when scaling AI engineering.

By Theo Valmis · Published May 2026 · Updated May 2026

## What RAG-based governance does

RAG (retrieval-augmented generation) applied to governance typically works like this: a vector store is indexed with ADRs, coding standards, architecture docs, or decision records. When a developer asks the AI to generate code, the relevant documents are retrieved and injected into the context window alongside the task.

The premise is sound: better context produces better output. If the model has the relevant architectural decision in front of it, it is more likely to comply with it.

- Improves context relevance by surfacing applicable rules at query time
- Reduces the noise of injecting every rule into every session
- Works with existing vector infrastructure (Pinecone, pgvector, Chroma, etc.)
- Scales to large decision corpora without blowing context windows
- Produces better average-case compliance than flat instruction files

RAG-based approaches are a genuine improvement over stuffing every rule into a CLAUDE.md file. They solve the context quality problem. They do not solve the enforcement problem.

The retrieval ceiling: A governance system that depends on probabilistic retrieval and model compliance is not a governance system. It is a better suggestion system. The model can still violate a retrieved rule. The violation is just slightly less likely.

## Where RAG ends and enforcement begins

Dimension | RAG-based approach | Mneme HQ
--- | --- | ---
Mechanism | Retrieves and injects context | Blocks Edit/Write at violation point
Enforcement model | Probabilistic — model may comply | Deterministic — violation is blocked
Rule application | Top-k retrieved, model-interpreted | Scope-matched, precedence-resolved
Failure mode | Wrong rule retrieved; model ignores rule | Override requires explicit decision record
Conflict resolution | Model interprets conflicts implicitly | Precedence engine resolves explicitly
Audit trail | No native provenance | Every enforcement event is traceable
Autonomous agent support | Rules dilute over long context | Hook fires per operation regardless of context length

## The layer model

RAG and governance enforcement are not competing tools. They operate at different layers of the AI engineering stack. The confusion arises because both involve “making the model aware of architectural decisions.” But awareness and compliance are different properties.

LLM generation produces output
RAG / context retrieval improves context quality
Governance enforcement (Mneme) blocks violations deterministically
ADRs / decision corpus source of truth

RAG improves the quality of what the model produces by surfacing relevant context. Mneme constrains what the model is permitted to produce by enforcing typed architectural decisions. These are complementary operations on the same underlying corpus.

A team that uses RAG without Mneme has better-informed generations that can still violate constraints. A team that uses Mneme without RAG has enforced constraints against a baseline context that may be less targeted. The combination provides both quality and compliance.

## Why enforcement cannot be probabilistic

The core argument for governance infrastructure over RAG-only approaches comes down to a single property: determinism.

A governance system must be correct every time, not most of the time. RAG retrieval is approximate by design: the right rule may not appear in the top-k results, and even when it does, the model applies it under competing task signals in a context window that is filling with tool outputs, prior turns, and intermediate reasoning.

Consider what happens with a RAG-based approach in an autonomous agent workflow with retries and multi-step orchestration. The rule was retrieved in step 1. By step 8, the context has grown. The model’s attention to the retrieved constraint has shifted. A violation in step 10 is not caught because nothing blocked it — the context just drifted.

Mneme hooks into every Edit and Write operation. The governance check runs per operation, not per session. There is no context drift. The enforcement surface does not dilute as the context window fills.

## When teams reach the RAG ceiling

Teams typically hit the RAG ceiling for governance in one of three ways:

- Multi-model drift. Different models retrieve the same rule and apply it differently. Switching from one model to another changes compliance behavior even with identical context. Enforcement that runs independently of model behavior is insulated from this.
- Autonomous agent violations. Rules retrieved at session start lose influence over long agent runs. A violation in step 12 of an orchestrated workflow is invisible to a rule read in step 1.
- Contested decisions. When a rule is ambiguous or in conflict with another, RAG surfaces both and the model picks. A precedence engine resolves the conflict deterministically based on scope and authority.

None of these are fixable by improving retrieval quality. They are structural properties of probabilistic enforcement. The fix is a layer below retrieval: a hook that evaluates the actual edit against the actual rules and blocks before the violation reaches the codebase.

## Using both together

The right architecture uses RAG and governance enforcement at their respective layers:

1. Build a typed decision corpus (ADRs, governance decisions, scope rules). This is the shared source of truth for both systems.
2. Use RAG to surface relevant context at generation time, improving the model’s awareness of applicable decisions. Better context produces better average-case output.
3. Use Mneme to enforce the hard constraints at operation time. When the model generates an Edit or Write, Mneme checks it against the decision corpus deterministically and blocks violations.
4. Track overrides explicitly. When a developer overrides a governance decision, the override is itself a decision record — with rationale, scope, and provenance. RAG can surface override history in future sessions.

In this architecture, RAG and Mneme are not substitutes. They are sequential layers operating on the same corpus. Retrieval improves generation quality. Enforcement guarantees architectural integrity. A codebase governed by both has better outputs and harder compliance guarantees than either provides alone.

## Frequently asked questions

Can RAG enforce architectural constraints?
RAG can surface relevant constraints as context for the model to consider. It cannot prevent the model from violating those constraints. A model given a RAG-retrieved rule can still generate code that breaks it — especially under task completion pressure or when the rule competes with other signals in a long context window. Enforcement requires a blocking layer, not a retrieval layer.
Does Mneme HQ use RAG internally?
Mneme uses deterministic retrieval: given a file path or edit operation, it looks up which architectural decisions apply based on scope rules and precedence semantics. This is not probabilistic embedding search — it is structured lookup against a typed decision corpus. The retrieval is exact and reproducible, not approximate.
Should we use both RAG and Mneme HQ?
Yes, at different layers. RAG is appropriate for surfacing documentation, historical context, and relevant examples to improve generation quality. Mneme is appropriate for enforcing architectural decisions that must not be violated regardless of generation quality. The two operate on different problems: RAG improves what the model produces, Mneme constrains what the model is allowed to produce.
Why does retrieval quality matter less than enforcement for governance?
A governance system must be correct every time, not most of the time. RAG retrieval is probabilistic: the right rule may not be in the top-k results, and even when it is, the model may not apply it under competing task signals. Governance infrastructure must be deterministic — the violation either happens or it does not. That property requires enforcement, not retrieval.
Related governance concepts

- Architectural compiler

  The pipeline that converts documentation-form decisions into machine-evaluable constraint records.

- Precedence semantics

  Ordered rules that resolve overlapping decisions deterministically — supersession, scope, recency.

- Deterministic enforcement

  Same input, same verdict — every run, every agent, every consumer. The precondition for auditability.

- Governance infrastructure

  The platform layer that encodes, distributes, versions, and enforces decisions across agents and CI.

- Decision memory: where architectural authority lives

  The canonical definition of decision memory and why retrieval, documentation, and generic memory cannot store authority.