Evaluating LLM Output Quality In Production

Short summary

LLMs silently drift in production without code changes—a Stanford study showed GPT-4's accuracy plummeting from 97.6% to 2.4% on identical tasks between March and June 2023. Production quality requires three-layer evaluation: offline evals with hand-curated golden datasets, reference-free checks like hallucination detection, and continuous monitoring for drift. Scoring methods span from cheap exact-match for deterministic answers to expensive LLM-as-judge for subjective content, each with specific tradeoffs.

• •LLM behavior drifts silently over time without code changes; Stanford/Berkeley research documented GPT-4 accuracy collapse from 97.6% to 2.4% on identical tasks
• •Three-layer evaluation: offline regression evals with golden datasets, reference-free checks for hallucinations, and continuous production monitoring
• •Scoring methods range from cheap exact-match (deterministic answers) to expensive LLM-as-judge (subjective content); each has speed/flexibility tradeoffs

Generated with AI, which can make mistakes.