Validate AI Analytics Output in Claude Code with AI

Free Workshops, Full Courses, Consulting Engagements: https://aianalystlab.ai AI can now run an end-to-end analysis in minutes — and hand you a wrong answer with exactly the same confidence as a right one. This lesson shows how to check work you didn't do yourself, then runs a live reliability eval in Claude Code that catches a hidden metric-definition problem before it reaches a deck. In this live lesson, Shane Butler runs the demo while Hai Guan and Sravya Madipalli co-host, reframing "evals" as one plain idea: a check that tells you how much to trust an answer you didn't work out yourself. Trust is a dial, not a switch — match the rigor of your check to the stakes of the decision. You'll learn why an agentic analysis is a 7-step pipeline (not one box), where it goes wrong at every step, and the 4 ways to check any output: compare to a known answer (ground truth), grade against a rubric, ask several ways and triangulate, and show the receipts. Then watch a reliability skill fire the same question through 5 sub-agents against a NovaMart Snowflake warehouse: "checkout conversion" comes back rock-stable at 33.2% because a metric dictionary pinned the definition — but "retention rate" scatters from 9.7% to 99.3%, exposing that the team never agreed on what retention means. Agreement is reassurance, not proof. Stable is necessary, not sufficient. Live demo: invoking a reliability eval skill in Claude Code that fires 5 independent sub-agents at a NovaMart Snowflake warehouse, logs every SQL query through a hook for an audit trail, and computes whether the answer is stable or drifting — stable for checkout conversion, scattered for retention. Timestamps: 0:00 Intro — welcome, instructor intros, and what today is about 3:23 Getting the answer stopped being the hard part — now you have to trust it 5:00 Read of the room — Claude Code experience and roles 7:30 Why a wrong AI answer doesn't look wrong, and what it costs you 11:44 Where does AI analysis go wrong? A room-sourced list 14:00 Evals in one sentence — trust an answer you didn't work out yourself 15:30 Trust is a dial, not a switch — match the check to the stakes 17:21 An agentic analysis is a 7-step pipeline, not one box 22:32 The 4 ways to check any output — known answer, rubric, triangulate, receipts 24:30 Two caveats — agreement is reassurance; don't check everything the same 27:46 Demo setup — predict it: same question 5 times, same answer? 29:58 The reliability skill — 5 sub-agents, Snowflake, and a SQL audit hook 33:00 Checkout conversion comes back stable — the dictionary pinned the definition 39:00 Retention scatters 9.7%–99.3% — a hidden metric-definition problem 47:00 What reliability did and didn't do — stable is necessary, not sufficient 49:21 Worksheets, the 101 bootcamp, the 201 evals course, and promo codes 53:21 Q&A — aligning metric definitions across a team 55:43 Q&A — data security, anonymization, and restricted environments 58:14 Q&A — open source models and self-hosting 1:01:56 Q&A — semantic layers and cutting corners responsibly 1:04:08 Will models get good enough that we won't need evals? What's next: Free workshop — Pressure-Test Any AI Analysis (what to do when there's no answer key), Wed Jun 24: https://maven.com/p/167951/pressure-t... 101 bootcamp — Build Agentic Analytics 101 in Claude Code (build your own AI analyst), Jul 13–17: https://maven.com/dataneighbor/build-... 201 bootcamp — Agentic Analytics 201: Validation & Context Management (built entirely on today's topic), Jun 27–Jul 4: https://maven.com/dataneighbor/advanc... 5-week course — AI Analytics for Everyone: https://maven.com/dataneighbor/ai-ana... ——— More from AI Analyst Lab: Subscribe:    / @dataneighborpodcast   Newsletter, workshops, courses: https://aianalystlab.ai Spotify: https://open.spotify.com/show/3BZnavS... Apple Podcasts: https://podcasts.apple.com/us/podcast... Hosted by Hai Guan, Shane Butler, and Sravya Madipalli.