What is LLM evals (evaluation)?
LLM evaluation measures answer quality and safety. You compare outputs against expectations (ground truth or rules) and track metrics like faithfulness (did the answer only use the provided source data?) and accuracy (is the answer factually correct?). With evals, you can safely change prompts, models, or data without surprises.
How does it work?
Create test sets, define checks (exact match, semantic similarity, policy rules), and run them automatically on changes. Use dashboards to spot regressions and approve releases.
When does it matter? (Examples)
- Stakeholders ask, “Can we trust the answers?”
- You plan to switch models or prompts and need guardrails.
- Regulated workflows require evidence of testing.
Benefits
- Builds trust with evidence
- Catches regressions early
- Supports compliance
Risks
- Overfitting tests to happy paths
- Ignoring edge cases
- Manual reviews that don’t scale
Antire and LLM evals (evaluation)
We set up eval pipelines, define business‑relevant metrics, and wire results into CI/CD so changes are safe and measurable.
Services
Related words
Faithfulness, RAG evaluation, LLM observability, Precision/Recall, Test set, Guardrails