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.
Error analysis typically involves annotating traces, identifying patterns, and prioritizing which failures need to be addressed.
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 this approach misses a critical step: You can't know what to measure without first understanding what's actually going wrong in your product.
Generic eval tools measure general metrics, but they won't catch the specific problems your AI product has. For instance, you might discover through error analysis that your interview coaching AI suggests leading questions—a domain-specific error that no off-the-shelf tool would test for. Surface-level review or even positive user feedback won't reveal these issues. You need systematic annotation to find them.
How does error analysis work?
Error analysis is rooted in grounded theory—a qualitative research method where you build understanding from real data rather than starting with assumptions. You collect your traces (inputs and AI outputs), then annotate them to identify what went wrong and why.
As you review traces, you look for patterns in the failures. These patterns become your error categories. Once you've identified your most common failure modes, you can design evals—whether code-based or LLM-as-Judge—to measure how often each type of error occurs. This measurement lets you track whether changes to your product are actually reducing errors.
Learn more:
- 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
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