How to Evaluate LLM APIs Before Production
Choosing an LLM from leaderboard results is risky. Your product has its own prompts, users, data, constraints, and quality bar.
A useful LLM evaluation tests models against real tasks before production traffic moves.
Build a representative test set
Collect examples from:
- customer support conversations
- product workflows
- internal tools
- failed prompts
- edge cases
- common user questions
- high-value tasks
Include easy, typical, and difficult cases.
Define scoring criteria
Score models on:
- correctness
- completeness
- tone
- format compliance
- reasoning quality
- refusal behavior
- hallucination risk
- latency
- cost
One overall score is less useful than category-level scores.
Test structured output
If your app needs JSON, tool calls, or extracted fields, validate outputs automatically.
Track:
- schema pass rate
- missing fields
- invalid JSON
- hallucinated fields
- retry success rate
This is often more important than prose quality.
Compare cost per successful task
Do not compare only token price. Compare cost per successful answer.
A cheaper model may need more retries. A stronger model may be cheaper if it succeeds on the first attempt.
Test latency
Measure latency from your production environment, not your laptop. Include time to first token and total completion time.
Final thoughts
LLM evaluation should be practical, repeatable, and tied to product outcomes. Use real prompts, score multiple dimensions, validate structured output, and compare cost per successful task.