Error Analysis
What is error analysis?
Error analysis is the process of systematically reviewing your AI product's outputs (traces) to identify what mistakes it's making and categorize the most common failure modes. This analysis is essential because it tells you what errors to measure with evals—errors are use case specific, so you can't rely on generic tools to catch your particular product's problems.
Why do teams skip error analysis and why does that matter?
Most people skip error analysis and jump straight to buying eval tools or building tests. But you can't know what to measure until you know what's going wrong. Generic tools won't catch your product's specific problems—like an interview coach that suggests leading questions—and positive user feedback won't surface them.
How does error analysis work?
Error analysis is rooted in grounded theory: you build understanding from real data, not assumptions. Collect traces (inputs and AI outputs) and annotate each one with what went wrong, in plain language. Patterns in those notes become your error categories. Then prioritize them by frequency and severity.
How rigorous does error analysis need to be?
It depends on who a bad response affects. For a personal workflow, a quick pass is enough. When ChatGPT turned 17 Lovable interview transcripts into stories, I read one output against its transcript. It got details wrong and fabricated quotes. That was enough to define two evals—a fact checker and a hallucination guard—for the other sixteen.
For a customer-facing product, be rigorous. I released my Interview Coach in beta to collect hundreds of traces, then annotated, categorized, and prioritized the errors. Use your discovery habits to decide which errors matter most to customers. Do this before you pick an eval type: it tells you which errors to count.
Learn more:
- AI Evals: A Hands-On Guide for Product Teams
- How I Designed & Implemented Evals for Product Talk's Interview Coach
- Behind the Scenes: Building the Product Talk Interview Coach
- Building My First AI Product: 6 Lessons from My 90-Day Deep Dive
Related terms:
Last Updated: September 3, 2026