Evals for AI Engineers

Chapter 3. Error Analysis

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Figure 3-1. Anthropic’s visualization of the iterative process for creating strong empirical evaluations. This cycle emphasizes developing test cases, engineering and refining prompts, and testing against both initial cases and held-out evaluations. Image source: Anthropic Anthropic 2024.

The process of developing robust evaluations for LLM applications is inherently iterative. It involves creating test cases, assessing performance, and refining the system based on those observations. High-level guides, such as Anthropic’s documentation on creating empirical evaluations for Claude Anthropic 2024, often depict the evaluation process as a cycle of developing test cases, engineering prompts, testing, and refining (Figure 3-1).¹ This section, and indeed our overall “Analyze-Measure-Improve” lifecycle (Figure 1-2), provides a detailed, step-by-step methodology for the Analyze portion of this iterative loop—​specifically focusing on ...