Design the post-FT eval suite for a customer-support fine-tune
Design the post-fine-tune evaluation suite for a customer-support model derived from a 7B instruction-tuned base. Cover the four buckets it should include, why each is needed, and one concrete metric per bucket.
Gate the release on four buckets: target-task golden set, format adherence, capability regression versus base, and safety in both directions. Any one fails, you do not ship.
Imagine a new hire who just finished a training course for your support desk. Before you let them answer real customers, you check four things. Can they actually solve typical tickets? Do they fill out the form correctly every time, in the right tone? Did the course make them forget basic skills they had before, like reading or doing simple math? And do they stay safe, refusing the dodgy requests while NOT being so paranoid they reject normal questions too? You only hand them a headset once all four checks pass. A fine-tuned model is the same: each check is a separate test set with its own score, and the release gate says every bucket must clear its bar.
Detailed answer & concept explanation~7 min readEverything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example.
Everything important, quickly.
5 min: why one score is not enough + the four buckets with a concrete metric each + held-out contamination + over-refusal + the conjunctive ship gate and what to do when a bucket fails.
| Bucket | Why it matters | Concrete metric |
|---|---|---|
| Target-task golden set | Confirms the model does the actual job | Pass rate at rubric score at least 4 of 5 |
| Format adherence | Broken envelope fails downstream even if content is right | JSON parse rate plus tone-consistency score |
| Capability regression | Fine-tuning can erase general skills (forgetting) | Average absolute-point delta versus base |
| Safety, both directions | Must refuse violations and not over-refuse benign | Violation refusal rate plus XSTest acceptance rate |
Real products, models, and research that use this idea.
- OpenAI's fine-tuning API docs recommend a held-out validation split and warn about overfitting, the same regression discipline this suite enforces.
- XSTest is the standard benchmark teams run to catch over-refusal after safety fine-tuning, measuring acceptance on benign hard negatives.
- LangSmith and Arize Phoenix expose layered eval templates where a golden-set judge, format checks, and regression suites run as separate dashboards per checkpoint.
- OpenAI Evals and similar harnesses are commonly wired into CI so a fine-tuned Llama 4 or GPT-5.5 checkpoint is gated before it reaches production traffic.
- Anthropic-style safety evals pair a policy-violation refusal set with benign look-alike prompts so Claude Opus 4.7 fine-tunes are scored in both refusal directions.
What an interviewer would ask next. Try answering before peeking at the approach.
QHow do you stop your held-out golden set from being contaminated by the training data?
QYour LLM judge and your human raters disagree on the golden set. How do you reconcile and trust the gate?
Don't say thisRed flags and common mistakes that signal junior thinking. Click to expand.
Red flags and common mistakes that signal junior thinking. Click to expand.
Reporting only the target-task score and shipping. That misses regression on general capability and over-refusal, the two failures a single task metric is structurally blind to.
The night-before-the-interview bullets. Scan these on the way to the call.
Primary sources. Skim if you want the original framing.
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