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.
Concept explanation~2 min read
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
Concept explanation~2 min read
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
A freshly fine-tuned model is a liability until proven otherwise. Fine-tuning is a deliberate behavior edit, and every deliberate edit risks unintended side effects. The job of a post-fine-tune eval suite is not to produce one impressive number; it is to act as a release gate that catches each independent way the edit can go wrong before a single customer sees the model.
The reason a single quality score fails is structural, not lazy. A blended metric can rise on the target task while general capability quietly collapses, while the output format drifts off-schema, or while the model starts refusing perfectly benign questions. Those failures live on different axes, so they need different measurements. Average them together and the strong axis hides the weak one.
For a customer-support model derived from a 7B instruction-tuned base, four buckets cover the realistic failure surface: the target task, output format, capability regression, and safety in both directions. Each bucket needs its own uncontaminated held-out set and its own concrete metric. The deep dive below walks each bucket, then shows how to combine them into a gate that only opens when all four pass.
Bucket 1: the target-task golden set
This is the 'does it do the job' eval, and it is the one everybody remembers to build. The trap is building it badly. The golden set must be sampled from the real ticket distribution, not from whatever prompts were convenient. Stratify across the live categories: account, billing, technical, and the long tail of edge cases that generate the angriest customers.
Hand-curate 200 to 500 questions with reference answers. Score each fine-tune response against its reference using a fixed rubric covering correctness, tone, and completeness. You can use human raters for ground truth or a strong LLM judge for scale, but the judge prompt and rubric must be frozen so scores are comparable across checkpoints.
The concrete metric is pass rate at a rubric score of at least four out of five. The most important discipline here is that the held-out set never overlaps training data. Leakage through reused instruction templates is the single most common way this bucket lies to you, inflating the pass rate on questions the model effectively memorised.
Situations where this technique stops working.
2–4 min · Everything important, quickly.
| 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.
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?
Split by source and time, not by random row. Dedupe against training prompts and templates, since instruction-template reuse is the most common leak. Reserve genuinely unseen tickets and refresh the set periodically.
Red flags & common mistakes
The phrases that signal junior thinking. Click to expand.
Red flags & common mistakes
The phrases 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.
60 second bullets to scan on the way to the call.
The four buckets and what each one measures
Why a single blended score is insufficient
Primary sources. Browse if you want the original framing.
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