LLM evaluation pushed me to think about data collection with more nuance. Accuracy is rarely one number. Product quality depends on task type, user expectation, retrieval context, safety constraints, latency, and the cost of being wrong.

The evaluation data model matters. I want to know the prompt, model version, retrieved context, user intent, expected behavior, actual response, feedback, and any human review notes.

Without that structure, teams end up debating examples in isolation. With it, they can find patterns: weak retrieval, ambiguous instructions, bad source content, or model behavior that changed after a release.

LLM products need evaluation systems that look more like data products than test suites.

Evaluation as an Operating System

The hard part of evaluation is that product quality is multidimensional. A response can be factually correct but unhelpful, safe but too vague, fast but incomplete, or well-written but grounded in the wrong source. A useful evaluation system has to preserve those distinctions.

I like evaluation datasets that combine curated cases with production traces. Curated cases make regression testing possible. Production traces reveal what users actually ask, where retrieval fails, and which tasks are ambiguous. The two datasets answer different questions.

Human review also needs structure. If reviewers only leave free-form notes, the data is hard to analyze. If the rubric is too rigid, it misses nuance. Good evaluation design gives reviewers enough categories to create signal while still leaving room for explanation.

The biggest maturity step is connecting evaluation to release decisions. Teams should be able to ask whether a prompt, model, retrieval change, or tool update improved the product for specific task types. That requires durable data, not screenshots in a chat thread.

Building the Evaluation Habit

Evaluation should be part of the development loop, not a separate ceremony before launch. If a team changes a prompt, retriever, or model, it should be natural to run known cases and inspect slice-level results.

I also think teams need a shared language for failure. “Bad answer” is too vague. Was it ungrounded, incomplete, unsafe, verbose, stale, or mismatched to user intent? Clear categories make improvement work more focused.

The dataset should evolve with the product. New failure modes from production should become regression cases. High-value workflows should get deeper review. Rare but severe risks should be represented even if they are not common in traffic.

The goal is not to reduce product quality to one number. The goal is to build enough measurement discipline that teams can make better release decisions and learn from real use.