Research
Evaluating LLM Reliability (Without the Drama)
2025-01-05 · 6 min
A framework for measuring hallucinations, attribution quality and regressions that actually matter in production.
Reliability starts with defining failure modes. I separate factual errors, source mismatches and refusal gaps before any scoring begins.
A/B tests are useful but only if you track error types. Otherwise you will ship a regression with better vibes.
Evaluation sets need real user questions, messy inputs and adversarial cases. Synthetic prompts are fine for sanity checks, not for decisions.
If your model can’t say “I don’t know,” it will invent the rest. That’s not intelligence; that’s confidence.
- Track error categories, not just overall scores.
- Use real production queries in evaluation.
- Citations must be checked for alignment.
- Reject answers that lack evidence.