Industry Models & Fine-Tuning · Asset Pack
Prove the general model is insufficient before discussing fine-tuning. Most fine-tuning requests are really prompt and retrieval problems.
Lets data decide whether a general model suffices or fine-tuning pays.
A selection scorecard and evaluation method: test how a general model actually performs on your business question set first, then decide whether fine-tuning is worth the investment. The goal is adequate accuracy at minimal cost — not fine-tuning for its own sake.
If you are hitting these
- General models miss your industry terminology and answer off-target
- Hallucination rate already affects critical business judgments
- You're stuck on whether to fine-tune, and which model
You have no usable eval set — without one you cannot tell whether fine-tuning actually helped.
How to use it
The full path from inventory to acceptance; each step has a concrete output.
What's inside
Model selection scorecardEvaluation starter packHallucination test-set guideEvery deliverable in the pack — apply as-is, then adapt to your business.
Draw 50–100 questions from real business queries, including adversarial ones.
Test candidate models across scorecard dimensions, including cost and latency.
Use the general model if it passes; fine-tune only the weak spots.
Re-run the same eval set after fine-tuning to confirm measurable gains.
Acceptance standards
You've used this pack well when the following hold — they double as review criteria.
- 01
The eval set comes from real business questions, not public datasets
- 02
Candidates are compared on the same eval set
- 03
The fine-tuning decision has a general-model baseline score for reference
Output template preview
A skeleton of what the pack produces; the full template ships inside the pack.
| Dimension | Weight | Model A | Model B |
|---|---|---|---|
| Domain accuracy | 40% | 82 | 74 |
| Cost per call | 25% | … | … |
| Latency | 20% | … | … |
Verdict: A passes; no fine-tuning needed yet
Dashed rules mark example rows — replace them with your own data and the sheet is done.
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Industry Models & Fine-Tuning · Asset Pack
- Our stance
- Prove the general model is insufficient before discussing fine-tuning. Most fine-tuning requests are really prompt and retrieval problems.
- If you are hitting these
- General models miss your industry terminology and answer off-target; Hallucination rate already affects critical business judgments; You're stuck on whether to fine-tune, and which model
- When not to use it
- You have no usable eval set — without one you cannot tell whether fine-tuning actually helped.
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