# Personal AI Infrastructure -- Prompt Engineering Module

**Version**: v1.2.3 | **Status**: Active | **Last Updated**: March 2026

## Overview

Prompt Engineering Module provides comprehensive prompt lifecycle management including template authoring, version tracking, optimization strategies, and evaluation scoring. This is a **Service Layer** module.

## PAI Capabilities

```python
from codomyrmex.prompt_engineering import (
    PromptTemplate, TemplateRegistry, get_default_registry,
    PromptVersion, VersionManager,
    OptimizationStrategy, OptimizationResult, PromptOptimizer,
    EvaluationCriteria, EvaluationResult, PromptEvaluator, get_default_criteria,
    score_relevance, score_response_length, score_structure, score_completeness,
)
```

## Key Exports

| Export | Type | Purpose |
|--------|------|---------|
| `PromptTemplate` | Dataclass | Reusable template with variable placeholders |
| `TemplateRegistry` | Class | Collection management for templates |
| `get_default_registry` | Function | Module-level default registry |
| `PromptVersion` | Dataclass | Versioned template snapshot |
| `VersionManager` | Class | Version history with diff and rollback |
| `OptimizationStrategy` | Enum | CONCISE, DETAILED, CHAIN_OF_THOUGHT, FEW_SHOT strategies |
| `OptimizationResult` | Dataclass | Optimization result with token reduction estimate |
| `PromptOptimizer` | Class | Strategy-based prompt optimizer |
| `EvaluationCriteria` | Dataclass | Evaluation criterion with scorer and weight |
| `EvaluationResult` | Dataclass | Evaluation result with per-criterion scores |
| `PromptEvaluator` | Class | Multi-criteria prompt-response evaluator |
| `get_default_criteria` | Function | Default evaluation criteria preset |
| `score_relevance` | Function | Keyword overlap scorer |
| `score_response_length` | Function | Response length scorer |
| `score_structure` | Function | Response structure scorer |
| `score_completeness` | Function | Response completeness scorer |

## PAI Algorithm Phase Mapping

| Phase | Prompt Engineering Contribution |
|-------|--------------------------------|
| **THINK** | Prompt optimization via PromptOptimizer; strategy selection (CONCISE, DETAILED, CHAIN_OF_THOUGHT, FEW_SHOT); version diffing for prompt evolution analysis |
| **BUILD** | Template creation and registration via TemplateRegistry; version management via VersionManager; few-shot example configuration |
| **VERIFY** | Prompt-response evaluation via PromptEvaluator; multi-criteria scoring; response comparison and ranking |
| **LEARN** | Version history tracking; export/import of template collections; evaluation trend analysis |
| **EXECUTE** | Template rendering with variable substitution; bulk optimization of template collections |

## Architecture Role

**Service Layer** -- Prompt Engineering provides tools that PAI agents use to author, optimize, and evaluate prompts systematically. It integrates with the `model_evaluation` module for output scoring and the `llm` module for model interaction patterns.

## Navigation

- **Self**: [PAI.md](PAI.md)
- **Parent**: [../PAI.md](../PAI.md) -- Source-level PAI module map
- **Root Bridge**: [../../../PAI.md](../../../PAI.md) -- Authoritative PAI system bridge doc
- **Siblings**: [README.md](README.md) | [API_SPECIFICATION.md](API_SPECIFICATION.md) | [MCP_TOOL_SPECIFICATION.md](MCP_TOOL_SPECIFICATION.md)
