Most startup AI features should start with retrieval and a small eval set, not fine-tuning. Fine-tuning earns its place once retrieval has hit a ceiling and you can measure the improvement.
Retrieval is faster to ship
Retrieval lets you ground answers in your own documents without changing model weights. It is cheaper, faster to iterate, and easier to debug than fine-tuning.
- No training pipeline
- Citations come for free
- Easy to update content
- Auditable answers
When fine-tuning is the right call
Fine-tuning helps when the task is narrow, the format is strict, or retrieval has plateaued and you can measure improvement on a real eval set.
- Strict output format
- Domain-specific tone
- Latency-sensitive flows
- Plateaued retrieval quality
Run an eval set either way
Whichever path you pick, build a small eval set first. Without it you cannot tell whether a change made the product better or worse.
- Real user questions
- Citation correctness
- Latency budget
- Cost per answer
Build checklist
- Pick the workflow before the technique
- Build the eval set from real users
- Try retrieval first
- Measure citation correctness
- Only fine-tune when retrieval plateaus
- Set a per-user cost ceiling
Need help picking the path?
Trioprod scopes AI integrations around the workflow, not the model, and ships with evals and cost controls in place.