Comprehensive Prompt Module

Full-stack prompt management system covering prompt design, versioning, A/B testing, analytics, and team collaboration — designed for organizations running LLM-powered applications at scale.

When to Use

Deploy this module when:

• Managing 50+ prompts across multiple LLM-powered features
• Need A/B testing to optimize prompt performance in production
• Multiple team members editing and deploying prompts
• Require audit trails and version history for compliance

Use simpler approaches when:

• Small projects with < 10 prompts → version control in code
• Solo developer → prompt templates in configuration files
• Prototyping → hardcoded prompts are fine

Quick Start

Initialize Prompt Registry

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from prompt_module import PromptRegistry, PromptVersion

registry = PromptRegistry(
    backend="postgres",  # or "redis", "file"
    project="my-llm-app"
)

# Register a prompt
registry.create(
    name="code-review",
    template="""
    You are a senior {language} developer.
    Review the following code for bugs, security issues, and best practices.

    Code:
    ```{language}
    {code}
    ```

    Provide your review as structured JSON with fields:
    issues (array), suggestions (array), overall_score (1-10).
    """,
    variables=["language", "code"],
    metadata={
        "author": "team-ai",
        "category": "code-quality",
        "model": "claude-sonnet-4-20250514"
    }
)

Use Prompts with A/B Testing

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# Get prompt with automatic A/B variant selection
prompt = registry.get("code-review", ab_test=True)

# Render with variables
rendered = prompt.render(language="python", code=user_code)

# Send to LLM
response = llm_client.complete(rendered)

# Log result for A/B analysis
registry.log_result(
    prompt_name="code-review",
    variant=prompt.variant_id,
    metrics={
        "user_rating": 4.5,
        "response_time_ms": 1200,
        "format_valid": True
    }
)

Version Management

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# Create new version
registry.update(
    name="code-review",
    template="...(updated template)...",
    changelog="Added security-specific review criteria"
)

# Rollback to previous version
registry.rollback("code-review", version=2)

# Compare versions
diff = registry.diff("code-review", version_a=2, version_b=3)

Core Concepts

Prompt Lifecycle

Design → Register → Test → Deploy → Monitor → Iterate
  |         |         |       |         |         |
  v         v         v       v         v         v
Template  Version   A/B    Traffic   Analytics  New
authoring control   test   routing   dashboard  version

A/B Testing Framework

Component	Purpose
Variant allocation	Random assignment with configurable split
Metric collection	Track quality, latency, cost per variant
Statistical analysis	Significance testing before declaring winner
Auto-promotion	Winning variant becomes default

Prompt Organization

Project
  ├── Category: code-quality
  │   ├── code-review (v3, active)
  │   ├── bug-detection (v1, active)
  │   └── refactoring-suggestions (v2, active)
  ├── Category: customer-support
  │   ├── ticket-classifier (v5, active)
  │   └── response-generator (v2, A/B testing)
  └── Category: content
      ├── blog-writer (v1, draft)
      └── summarizer (v4, active)

Configuration

Parameter	Default	Description
backend	"postgres"	Storage backend for prompts
project	—	Project identifier
ab_test_split	50/50	Default A/B traffic split
min_samples	100	Minimum samples before A/B conclusion
confidence_level	0.95	Statistical significance threshold
version_retention	30	Number of versions to keep
audit_log	True	Enable change audit trail

Best Practices

1. Version every change — never edit prompts in-place in production
2. A/B test before full rollout — even small prompt changes can have outsized effects
3. Define metrics upfront — decide what "better" means before running tests
4. Use semantic versioning — major changes (new format), minor changes (wording), patches (typos)
5. Centralize prompt management — scattered prompts in code become unmaintainable at scale
6. Include rollback procedures — when a new prompt version causes regressions

Common Issues

A/B test shows no significant difference: Increase sample size. Check that your metrics actually capture quality differences. The prompts may be functionally equivalent — test a more divergent variant.

Prompt versioning conflicts: Use locking when multiple editors modify the same prompt. Implement merge/review workflows similar to code pull requests.

Analytics overhead: Log metrics asynchronously. Sample high-volume prompts (log 10% of invocations). Aggregate metrics in batches rather than real-time.