A practical guide to evaluating AI systems in production. Patterns and frameworks from systems serving millions of users and processing billions of queries.
Greg Brockman (OpenAI Co-founder) • 361K views
Most AI evals are wrong. They measure the model when they should measure the system. Read Chapter 0: Why Evals Exist →
A narrative arc through evaluation in 8 chapters.
What breaks without evals. Why benchmarks collapse.
Architecture → failure modes → risk mapping.
Consequence weighting, critical journeys, testable hypotheses.
RAG, Agents, LLM-as-Judge—system-specific eval patterns.
Golden sets, synthetic vs real data, refresh strategies.
Error taxonomies, metrics that lie, trusting trends.
PM dashboards, exec narratives, trust signals.
CI/CD, canaries, release gates, pipelines.
Interactive tools, checklists, and templates to speed up your eval workflow.
32-point interactive launch checklist
Curate your first test dataset
Builder for scoring criteria
Score your team's eval capabilities
Quantify the value of evals money
Feature matrix for top eval tools
Cheatsheet for judge prompts
Template for stakeholder updates
Prioritize risks by impact
Anonymized patterns from production AI systems.
How query distribution shifted from 70% generic to 40% edge cases in 3 months—and how we caught it before users complained.
When "inspection" means different things to different clients. How domain-specific terminology broke our embeddings—and how we fixed it.
Chain-of-thought verification, citation requirements, and confidence scoring that cut hallucination rates by 75%.
Production-ready patterns you can use today.
I'm Saiprapul Thotapally, an AI Product Manager who's spent years building and evaluating AI systems in production—from RAG systems processing 10M+ queries/month to multi-agent pipelines validated by aerospace R&D teams.
This handbook distills patterns I've learned the hard way: that accuracy metrics lie, that production data drifts faster than you expect, and that the difference between AI that works and AI that fails is almost always in the evaluation.
All examples are anonymized. All code is open source. If this helps you build better AI systems, that's the goal.