Evaluating LLM systems

A model that tops a public leaderboard can still be wrong in the specific way that matters to your business. Benchmarks measure general capability. Your system needs evaluation built for your task.

This is the single most under-built piece of enterprise LLM integration. Teams ship on vibes (it looked good in testing), and then discover the failure modes in production, where they are expensive. A proper evaluation harness moves that discovery to before launch and keeps it there.

Start with ground truth

Evaluation begins with a set of real examples, labelled by people who understand the domain. Not synthetic prompts: actual inputs the system will see, paired with the answer a competent human would give. A few hundred well-chosen examples are worth more than ten thousand generic ones.

This ground-truth set is an asset. It encodes what "good" means for your task in a form a machine can check. Treat it like code: version it, grow it, and add every interesting failure you find in production back into it.

Measure the behaviors that matter

A single quality score hides more than it reveals. Real systems need to be measured along several axes at once:

• Accuracy: is the answer correct against ground truth?
• Grounding: for retrieval systems, is every claim supported by a cited source?
• Refusal: does the system decline when it should, and not when it should not?
• Cost and latency: is the quality worth what it costs to produce, at the speed users need?

A single quality score hides more than it reveals.

The harness is infrastructure

An evaluation suite is not a one-time exercise before launch. It runs in continuous integration, so a prompt change that breaks accuracy fails the build. It runs on a schedule in production, so drift surfaces as a metric, not a complaint. It is the instrument that lets you change the system with confidence, and in a field where models update constantly, that confidence is the whole game.

Make it legible to non-engineers

The best eval harnesses produce a report a risk officer can read. When you can show "here is how the system performs on these labelled cases, here is how that has changed since the last release," you turn an abstract worry into a concrete, reviewable artifact. That legibility is what gets the system through the rooms where it would otherwise stall.