A/B Testing LLM Systems

When Your New Model "Looks Better" but the Metrics Disagree You swapped in a new embedding model. Responses feel sharper. Your team is excited. You ship it. Two weeks later, task completion is down 8%. You have no idea why, and no way to trace it back to the change. This is the most common way LLM improvements go wrong. The new version looks better in demos, passes the vibe check, and fails silently in production. A/B testing is how you stop guessing and start knowing. Why LLM A/B Testing Is Harder Than Normal A/B Testing In a standard web A/B test, you change a button color and measure clicks. The metric is immediate, unambiguous, and causally close to the change. LLM systems have three properties that make this harder: Evaluation lag. Whether a response was actually helpful often isn't clear until the user does (or doesn't) complete their task — which might be minutes or sessions later. Multi-component pipelines. Changing the embedding model affects retrieval quality, which affects g