Generative-AI: Usecase-Specific Evaluation of LLM-powered Applications
summary
Presents a practical framework that evaluates LLM applications on quality, price, and speed by building use-case benchmark sets with subject-matter experts, creating LLM-based judging tools, then performing supervised alignment to human labels; out-of-the-box judges yield about 30–50% agreement with humans but iterative tuning raises agreement substantially. Recommends collecting judges’ reasons, using pass/fail KPIs for decisive signals, holding out test sets to avoid overfitting, and deploying reviewing agents alongside generators for continuous monitoring and user-facing reliability signals.
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chapters (12)
- 0:00Introduction
- 1:11LLM-powered agent evaluation basics
- 3:15LLM vs agent evaluation
- 5:21Agent evaluation dimensions
- 8:11Use-case-specific evaluation components
- 10:15Benchmark dataset curation workflow
- 13:51Benchmark dataset structure
- 16:22Developing LLM judge tools
- 19:08Supervised alignment of tools
- 21:39Using tools in products
- 24:04Recap and final takeaways
- 25:27Q&A
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