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.
Free · 2 AI evals / day
When you **fine-tune** a model for customer support, you are deliberately changing its behavior. The eval suite has to confirm you changed the right things and broke nothing else.
Start with the obvious bucket: does it actually answer support tickets well? Build a golden set of 200 to 500 real questions across the true ticket mix (billing, account, technical, edge cases), and score each answer against a reference with a rubric.
Next, check format. Support replies usually need a fixed shape, a JSON envelope, a length cap, a brand tone, no leaked customer data. Correct content in a broken shape still breaks your product, so parse every output and measure the pass rate.
Third, check that fine-tuning did not erase general skills. The base model could reason, do light math, follow instructions. Re-run a general benchmark on both base and fine-tune, and watch the score delta for catastrophic forgetting.
Finally, check safety in both directions: it must refuse genuinely unsafe requests, but must NOT over-refuse normal questions. Gate the release on all four buckets passing.
A post-fine-tune eval suite is a release gate, not a single number. I would build four independent buckets, each with its own held-out set and metric.
First, the target-task golden set. Hand-curate 200 to 500 questions sampled from the real ticket distribution. Score each answer against a reference with a fixed rubric covering correctness, tone, and completeness. Use human raters or a strong LLM judge. Metric: pass rate at score at least four out of five.
Second, format adherence. Support outputs need a strict shape: JSON envelope, length cap, brand tone, no echoed customer data. Run a few hundred prompts and parse them. Metric: schema parse rate plus a tone-consistency score.
Third, capability regression. Fine-tuning can erase general skills the base had. Run a shared benchmark on base and fine-tune. Metric: average absolute-point delta, where a drop past two or three points flags forgetting.
Fourth, safety in both directions. It must refuse genuine policy violations and must NOT over-refuse benign look-alikes. Use a refusal-positive set plus XSTest for hard negatives. Metric: refusal rate on violations plus acceptance rate on the benign set.
The gate combines all four. If any single bucket misses its bar, the model does not ship.
Quick reference
TL;DR
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.
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.
A throwaway prototype that will never see real customer traffic A full four-bucket gate is wasted effort when nothing ships; a quick golden-set spot check is enough.
The fine-tune touched only output format, not knowledge or reasoning Format and a light regression check dominate; an elaborate safety battery adds little signal here.
You have fewer than a few hundred representative real tickets A golden set built from synthetic-only prompts will not reflect the live distribution and gives false confidence.
No fixed safety policy exists for the product yet Refusal evals need a defined policy; scoring refusals against an undefined bar produces meaningless numbers.
Quick scan · 2–4 min
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.
Side-by-side
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-world examples
Real products, models, and research that use this idea.
1OpenAI's fine-tuning API docs recommend a held-out validation split and warn about overfitting, the same regression discipline this suite enforces.
2XSTest is the standard benchmark teams run to catch over-refusal after safety fine-tuning, measuring acceptance on benign hard negatives.
3LangSmith and Arize Phoenix expose layered eval templates where a golden-set judge, format checks, and regression suites run as separate dashboards per checkpoint.
4OpenAI 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.
5Anthropic-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.
Interviewer follow-ups
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?
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QYour LLM judge and your human raters disagree on the golden set. How do you reconcile and trust the gate?
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QCapability dropped three points on MMLU but target-task pass rate rose. Do you ship?
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Don't say this
Red flags and common mistakes that signal junior thinking. Click to expand.
Most common mistake
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.