Enhancing Agent Reliability Through Evaluation-Driven Development
summary
Claim: roughly 95% fail-to-prod for AI projects. Presents an actionable evaluation loop: build small manual datasets, augment with synthetic LLM-generated cases, collect real user signals (thumbs up/down and implicit behaviour), then run LLM-as-judge semantic evaluators and numeric metrics (F1, pass@K, baseline deltas). Shows a concrete failure mode — tool-call hallucinations — and demonstrates remedy steps: dataset of happy/sad paths, evaluator that checks function parameters, metric-driven A/B of prompts/models/ensembles in an MLOps pipeline.
AI-written summary · about
chapters (9)
- 0:00Introduction
- 2:46AI projects fail in production
- 8:10Mindset shifts for AI success
- 10:32Evaluation loops in AI
- 17:07Building AI evaluators
- 20:56Metrics for evaluation loop
- 23:15AI evaluation workflow example
- 30:19Summary
- 31:07Q&A
related talks
25mEngineering Better Evals: Scalable LLM Evaluation Pipelines That Work
47mBuild Trustworthy AI Agents powered by Evals
24mHow to Test AI Features Before Your Users Do
15mPractical tactics to build reliable AI apps
29mGenerative-AI: Usecase-Specific Evaluation of LLM-powered Applications
38mBuild Evals That Actually Matter
38mMaking LLMs reliable - A practical framework
35mEvaluating LLM systems: the nuts and bolts