A Practical Guide to LLM Evaluation
summary
Practical steps for building task-specific evaluation: set data that matches your application (human-curated, synthetic, traffic), write scorable rubrics and encode them into prompts, and run three-layer evaluation using ROUGE, BLEU or embedding cosine for fast checks, humans as ground truth for quality, and LLM-as-judge auto-raters to scale. Use pairwise metrics like Cohen's Kappa for preference tasks, orchestrate multiple models, emit structured JSON evaluations, and expect human eval to be best but costly—combine methods for reliable production readiness.
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chapters (9)
- 0:00Introduction
- 0:43Why evaluation matters
- 3:36Task-specific evaluation
- 7:27Non-deterministic outputs
- 10:18Success criteria and rubrics
- 15:32Three evaluation methods
- 20:24Automated evaluation architecture
- 25:42Key takeaways
- 27:08Q&A
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