Evaluating Your Fine-Tune
Explore effective techniques to evaluate fine-tuned AI models and detect regressions before deployment. Learn how to run side-by-side tests with base models, enforce strict output contracts, and diagnose common failure modes to ensure your fine-tunes are production-ready and reliable.
Training a fine-tuned model (or a PEFT adapter like LoRA) is often the easy part. The real challenge is determining whether our adapter is actually production-ready or if it just feels better on a couple of lucky prompts.
A common failure in production occurs when an adapter improves tone or style but silently breaks downstream system contracts—generating invalid JSON, omitting mandatory object keys, or adding conversational commentary around structured data.
This lesson teaches you how to evaluate fine-tunes and detect regressions before deploying to production.
The core principles of fine-tune evaluation
Evaluating a fine-tuned model requires a disciplined approach distinct from standard model training:
Evaluate only on held-out prompts: We never test our model using prompts present in the training set. Memorization masks true capabilities; testing on held-out data reveals whether the model learned underlying patterns or simply memorized responses.
Prioritize the output contract: A beautifully written explanation that fails JSON parsing breaks the application pipeline. Structural integrity and contract compliance take precedence over narrative style.
Side-by-side paired comparison: Always run identical held-out prompts through both the Base Model and the Base + Adapter Model using identical decoding parameters (
temperature,top_p,max_tokens). ...