Annotation Tool
What is an annotation tool?
An annotation tool is software used to label, tag, and categorize data (such as AI traces, transcripts, or outputs) for the purpose of evaluation, training, or quality assessment. In AI product development, annotation tools help teams systematically review and label LLM outputs, identify failure modes, and create labeled datasets for building and validating evals. These tools provide structured interfaces for human reviewers to examine AI system behavior and mark specific characteristics or issues.
Why do AI product teams need annotation tools?
Annotation tools address a fundamental challenge in AI product development: understanding and improving non-deterministic system behavior. Unlike traditional software where bugs are clear-cut, AI systems can fail in subtle ways that require human judgment to identify and categorize. By systematically reviewing traces and labeling outputs, teams can identify patterns in failures, understand where the AI performs well or poorly, and build the labeled datasets needed to create effective evals.
For example, when evaluating an Interview Coach that provides feedback on customer interviews, an annotation tool helps reviewers mark which responses included leading questions, which missed key issues, or which provided helpful guidance—creating the data foundation for measuring and improving the product.
When should teams use annotation tools?
Teams should incorporate annotation tools when they need to understand AI product quality at scale. During early development, manual review of a handful of traces might be sufficient. But as the product evolves and the number of interactions grows, annotation tools become essential for systematic quality assessment. They're particularly valuable when building golden datasets for evals, conducting error analysis to find failure modes, and validating that LLM-as-Judge evals are actually measuring the right things.
Learn more:
- Behind the Scenes: Building the Product Talk Interview Coach
- How I Designed & Implemented Evals for Product Talk's Interview Coach
- Building My First AI Product: 6 Lessons from My 90-Day Deep Dive
Related terms: