Appendix I: The Prompt Stability & Regression Framework
For the VP of Engineering, the “Model Trap” is the biggest technical risk. If a team builds 1,000 prompts for Model A, and the company decides to switch to Model B, the resulting “Alien Logic” could break the entire system.
This framework provides a strategy for maintaining Prompt Stability.
1. Prompt Versioning (Git as Truth)
Never allow “Live Tweaking” of production prompts in a UI.
- Prompts are Code: Every critical system prompt must be versioned in Git.
- The .prompts/ Directory: Maintain a dedicated directory in each repo for YAML-based prompt definitions, including model parameters (temperature, top-p).
2. The “Cross-Model” Regression Suite
Before switching models (e.g., GPT-4o to Claude 3.5), you must run a Prompt Regression Test.
- Input Sampling: Use a set of 50 “Golden Inputs” (complex business requests).
- Verification Loop: Use a “Judge LLM” (a different, highly capable model) to grade the output of the new model against the “Golden Output” of the old model.
- Parity Score: Do not deploy the new model unless it achieves a 95% logic parity score.
3. The “Meta-Prompt” Abstraction
Avoid model-specific jargon in your core prompts.
- The “System Prompt” Layer: Separate the Intent (what we want) from the Formating (how this specific model likes to receive it).
- Use a shim layer to translate the core intent into model-tuned specific formatting at runtime.
Stability Checklist
- Prompt Anchoring: Are critical prompts linked to specific model-version snapshots (e.g.,
gpt-4-0613) rather than “latest”? - Deterministic Benchmarking: Do you have a set of unit tests that the AI must pass whenever a prompt is updated?
- Model Switch Cost Analysis: Calculate the ‘Token-to-Intent’ ratio. If Model B requires 2x more tokens to achieve the same result, the ‘cheaper’ model might be more expensive.
“Don’t build your house on a model’s ‘Update’ cycle. Build it on your own ‘Intent’ library.” — Venkatesh