Eval Chaos vs. Steady State: Managing Changes in AI Systems

Description

Evaluation tools like Braintrust, Arise, and LangSmith aren't just for running tests—they're version control systems for your entire AI evaluation process. Here's what matters: you're constantly changing your system (prompts, RAG pipeline, tools), and you're also evolving your evaluations. Your datasets update, your LLM judges get refined, your eval criteria shift as you learn what quality actually means for your product. Without versioning, you can't compare results meaningfully. Eval can feel chaotic when you're first building. But in steady state—when you're changing your system more than your evaluations—these tools bring order. They let you run suites of evaluations (LLM judges, code-based evals, whatever mix you need), aggregate scores across them, and track how system changes impact quality over time. Most evals are binary pass/fail. The aggregate score across your eval suite becomes your north star metric for product quality. These tools make that trackable and reportable, so you're not flying blind every time you update your prompt or switch retrieval strategies. What eval tooling are you using, and are you versioning everything? #LLMEvaluation #AIEngineering #LLMOps #AIProductDevelopment #MachineLearning #DevTools