error-analysis

Structured error analysis uses open coding and axial coding to categorize AI output failures. You run this process on a batch of AI responses to surface dominant failure modes and prioritize improvements.

Root-cause analysis identifies underlying issues in AI outputs by grouping failures into named categories using axial coding. This approach surfaces dominant failure modes to guide targeted improvements.

Open coding and axial coding are needed when evaluating AI outputs to surface free-form failure descriptions and group them into named categories. This helps rank top failure categories for product features and prompts.

Error analysis provides evaluation design recommendations based on identified AI output failures. You can use these recommendations to design targeted evals for prompts and evaluation datasets.

Structured error analysis works on product features, evaluation datasets, and prompts. It applies frequency analysis to rank top failure categories and diagnose root causes across these different AI outputs.

AI assistants misclassify user intent due to underlying failures surfaced through structured error analysis. Applying open coding and root-cause diagnosis identifies these dominant failure modes to guide improvements.