Evaluation harness
LLM evaluation is how you know a model actually does the job on your own data, before it ships and every time something about it changes afterward.
- Policy harness
- Model routing harness
- Data transfer harness
- Guardrails harness
- Security harness
- Evaluation harness
- Observability harness
- Cost and token harness
- Reliability harness
- Audit harness
What it controls
Evaluation runs a model, or several models, against tasks and data that represent your own use case, not a generic benchmark, and scores the results against criteria you define. It runs before launch, to decide whether a model is good enough and which one to use, and continuously afterward, to catch the moment a model update, a prompt change, or a data shift makes an answer worse.
Why it matters
A model that scores well on a public benchmark can still perform poorly on your own documents, your own terminology, or your own edge cases. And a model that works well today can get quietly worse after a provider update, a prompt tweak, or a change in the data it's working from, with nobody noticing until a customer does. Evaluation turns that into something you measure on a schedule instead of something you find out about after the fact.
What good looks like
- Evaluation runs against your own tasks and data, not just a public benchmark
- A clear, repeatable scoring method so results from different models or dates are comparable
- Regression checks run whenever a model, prompt or data source changes, not only once at launch
- Evaluation results feed back into which model gets routed to which task
How it fits with the other harnesses
Evaluation is what makes Model routing a decision instead of a guess: you route to the model that actually scores best on your tasks, and you know when that stops being true. It also gives Reliability something concrete to watch for, a quality regression, alongside outright failures like timeouts.
In practice
Real patterns on the Resources page that lean on this harness:
- Confidence-scored catalogue curation
- Simulation-based preparation intelligence
- AI agent evaluation in your own tenant
Frequently asked questions
How is this different from a public model leaderboard?
A leaderboard tells you how a model performs on generic, public tasks. Evaluation here runs on your own data and your own definition of a good answer, which is what actually predicts how a model will perform for you.
Does evaluation only happen before launch?
No. The same checks run continuously, so a regression from a model update, a prompt change, or a shift in your own data gets caught instead of discovered by a customer.
Who defines what counts as a good answer?
Your own team sets the scoring criteria for your own tasks. Zapper Edge runs the evaluation harness against criteria you define; it doesn't impose a generic standard of correctness.
Questions about your AI rollout?
Talk to us about which harnesses fit your models and data.