TL;DR

Production LLM testing checklist: accuracy tests (correct answers), hallucination detection (fact-checking), safety checks (harmful content), performance benchmarks (latency, cost), regression test suites (catch breaking changes), and continuous monitoring. Test before deployment, after updates, and continuously in production.

LLM Testing Checklist for Production (2026)

Use this checklist to ensure your LLM is production-ready. Cover accuracy, hallucinations, safety, performance, and regression testing.

Pre-Production Testing

  • Accuracy tests: Validate correct answers for representative inputs
  • Hallucination detection: Check for made-up information
  • Safety checks: Test for harmful, biased, or inappropriate content
  • Performance benchmarks: Measure latency and cost per request
  • Edge case testing: Test boundary conditions and unusual inputs
  • Regression test suite: Maintain baseline tests for future updates

Accuracy Testing

  • Test critical user paths (80%+ of user interactions)
  • Validate factual correctness against knowledge bases
  • Check for correct reasoning and logic
  • Measure accuracy rate (target: 90%+ for production)
  • Test domain-specific knowledge if applicable

Hallucination Detection

  • Fact-check outputs against source documents or knowledge bases
  • Check for specific claims (numbers, dates, names) that can be verified
  • Test for contradictions with training data or source material
  • Measure hallucination rate (target: <5% for production)
  • Use automated detection tools combined with manual review

Safety Testing

  • Test for harmful content (violence, hate speech, self-harm)
  • Check for bias in outputs (gender, race, religion)
  • Validate privacy compliance (no PII leakage)
  • Test prompt injection resistance
  • Measure safety score (target: 99%+ for production)

Performance Testing

  • Measure latency (p50, p95, p99 percentiles)
  • Calculate cost per request
  • Test under expected load (concurrent requests)
  • Validate rate limiting and error handling
  • Set performance targets (e.g., <2s latency for 95% of requests)

Regression Testing

  • Maintain test suite of 100+ representative inputs
  • Run regression tests on every model update
  • Track metrics over time (accuracy, latency, cost)
  • Set up automated regression tests in CI/CD
  • Update baselines when outputs legitimately improve

Continuous Monitoring

  • Monitor accuracy and hallucination rates in production
  • Track performance metrics (latency, cost, error rates)
  • Set up alerts for quality degradation
  • Collect user feedback on output quality
  • Review and update tests quarterly

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Frequently Asked Questions

What's the minimum test coverage I need for production?

Aim for 80%+ coverage of critical user paths, 100% coverage of safety-critical scenarios, and representative samples of edge cases. Don't aim for 100% coverage of all possible inputs—focus on high-value, high-risk areas.

How often should I run regression tests?

Run regression tests before every production deployment, after every model update, and continuously in production (monitor key metrics). Integrate automated regression tests into your CI/CD pipeline.

What metrics should I track in production?

Track accuracy rate, hallucination rate, safety score, latency (p50, p95, p99), cost per request, error rate, and user satisfaction scores. Set up alerts for quality degradation or performance issues.

Can I skip some tests if I'm in a hurry?

Never skip safety tests or critical path accuracy tests. You can reduce edge case testing or manual review for non-critical features, but always test safety, accuracy, and performance before production deployment.