Evals and Error Analysis

Definition

Evals and Error Analysis

Evals are repeatable measurements that test whether an AI system performs acceptably on representative tasks, edge cases and risk scenarios. Error analysis is the structured inspection of failures to identify patterns, root causes and improvement actions, such as better instructions, retrieval, data cleaning, tool design, guardrails or human review.

Why It Matters

An eval is a structured test of AI-system behaviour using representative inputs, expected properties and scoring methods. Scores may be deterministic, model-based or human. Evals should measure the application, not just the underlying model.

Evaluation-driven development states desired behaviour as evaluable criteria, measures a baseline, analyses errors, changes one thing and checks regressions. Evals play a role analogous to tests, while accounting for probabilistic output.

Error analysis means inspecting failures, clustering them into meaningful categories and deciding which change is most likely to improve the system. Aggregate scores without qualitative error analysis rarely indicate what to build next.

Failure mode

The recurring risk is treating this concept as a label rather than an engineering decision with trade-offs, evidence needs and operational consequences.

Evaluation check

Ask what observable evidence would show that this concept has been applied well in a real system, repository or learning exercise.

Related Concepts

• Evals as the spine
• Testing and testability

Further study: OpenAI, Evaluation best practices, and Hamel Husain, Your AI Product Needs Evals.