Learn how to build better AI applications with this introduction to LLM-as-a-judge evaluation! Key takeaways: ➡️ Start by observing real application data to understand failure modes before defining metrics. ➡️ Keep metrics specific, answerable, and actionable - avoid catch-all "quality" scores or having 10+ overlapping metrics. ➡️ When writing judge prompts, treat them like human annotation guidelines: keep context under 4K tokens, and don't overthink fancy prompting techniques. ➡️ Most importantly, create a golden dataset with human annotations to measure judge alignment through meta-evaluation. Elizabeth Hutton emphasizes that imperfect evals beat no evals, and this is an iterative process with two overlapping loops - one improving your application, another improving your evaluations. She also covers when to use code vs human vs LLM evals, explains pairwise vs direct scoring approaches, and shares practical pitfalls to avoid. Check out the full video: https://lnkd.in/gZsYwRxK
How to evaluate LLMs: Tips and best practices
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