Eval-Driven Development (EDD) for AI-Native Engineers
The article introduces Eval-Driven Development (EDD), a methodology for building and iterating on AI-powered applications, where the focus is on measuring and improving the model's performance rather than just testing for pass/fail.
Why it matters
EDD is a critical methodology for building robust and reliable AI-powered applications, where the focus is on measurable performance rather than just functional correctness.
Key Points
- 1EDD replaces traditional TDD (Test-Driven Development) for working with large language models (LLMs) whose outputs are probabilistic and can vary
- 2The key is to define success criteria upfront and build an 'eval harness' to measure the model's performance against those criteria
- 3Every change to the system, from prompts to model swaps, should go through the eval process to catch regressions
- 4The eval suite becomes the core differentiator, as it captures real-world edge cases and production feedback
Details
The article explains that when working with LLMs, the traditional TDD approach of asserting exact output matches doesn't work, as the model's responses are probabilistic and can vary. Eval-Driven Development (EDD) is presented as an alternative, where the focus is on defining success criteria upfront and measuring how well the model performs against those criteria. This involves building an 'eval harness' - a dataset of real-world examples, a grading system to score the model's outputs, and a runner to execute the evaluations. The author emphasizes that every change to the system, from prompt updates to model swaps, should go through this eval process to catch any regressions. Over time, the eval suite becomes the core differentiator, as it captures real-world edge cases and production feedback that is unique to the application.
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