Free Field Guide — 20 pages
You're shipping AI blind.
Here's Clarity.
Most AI teams ship features and pray. They have no evals, no failure taxonomy, no idea which users are getting bad outputs. They're flying blind — and they know it.
This field guide gives you the exact 3-step framework we use with enterprise AI teams to go from "vibes-based QA" to measurable, repeatable improvement — in under a week.
Free Field Guide — 20 pages
You're shipping AI blind.
Here's Clarity.
Most AI teams ship features and pray. They have no evals, no failure taxonomy, no idea which users are getting bad outputs. They're flying blind — and they know it.
This field guide gives you the exact 3-step framework we use with enterprise AI teams to go from "vibes-based QA" to measurable, repeatable improvement — in under a week.
What’s inside
The Minimum Viable Evals roadmap — 3 phases to go from zero to production-grade evaluation
Error analysis with open coding + axial coding — the qualitative research method most AI teams skip
Why binary pass/fail beats Likert 1-5 scales (and requires smaller sample sizes)
RAG evaluation framework: retrieval metrics, generation quality, and domain-specific checks
Evaluating agentic workflows end-to-end — task success, step diagnostics, transition failure matrices
Guardrails vs. evaluators — when to block in real-time vs. measure async
Based on evaluation frameworks from Parlance Labs, Hamel Husain & Shreya Shankar, Eugene Yan, and Arize AI — distilled into a practical playbook by the Epistemic Me team.