AI Evaluation and Tracing
LLM evaluation framework: what it measures and how a team runs one
A plain explanation for teams that need to know whether an AI system is working, and how Kriv AI sets evaluation up around it.
An LLM evaluation framework is a repeatable way to test whether a language-model system does what it is meant to do. It combines a set of test cases with known good outcomes, the measures you score them on, and a rule for what happens when a score drops. Teams run it before release and again after every change to a prompt, model, data source or tool.
plain answer
What an LLM evaluation framework is
An LLM evaluation framework is the set of test cases, scoring measures and release rules a team uses to decide whether a language-model system is good enough to ship and to keep running. It replaces "it looked fine in a demo" with a result you can repeat.
Three parts make it a framework rather than a one-off test. First, an evaluation set: real examples with the outcome you expect. Second, measures: the things you score each answer on. Third, a rule: what score blocks a release, and who decides.
explainer
What an evaluation measures
What you measure depends on the job the system does. Common measures fall into a few families.
Correctness: did the system give the right answer, route to the right place or extract the right field, checked against labelled examples. Safety: did it avoid toxic content, leaking personal data or policy violations. Operations: how long it took, how often it failed, how often a person had to step in, and what each request cost. Format: did the output follow the structure that downstream systems expect.
Choose the measures from the business job first. A system that triages emails needs routing accuracy. A system that answers questions needs factual accuracy and a safe refusal. Measuring everything dilutes the signal.
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How a team runs an evaluation, step by step
1. Write down the goal and the acceptance rule for each workflow before building: what counts as good enough, and which failures are never acceptable.
2. Build an evaluation set from real cases such as tickets, emails, chats and documents, with private data masked, and label the expected outcome for each. Include the awkward edge cases and the failures found in pilots.
3. Record a baseline by scoring the current system, so every later change is compared with something real.
4. Run the evaluation on every change to a prompt, grounding data, model or tool configuration, and block the release if a critical measure gets worse.
5. Release in stages. Run the new version quietly alongside people (shadow mode), then expose it to a small group, then widen.
6. Keep watching after release. Log each interaction with the prompt version and model used so you can trace a bad answer back to its cause, and feed new failures back into the evaluation set.
7. Keep a last-known-good version ready and a clear path to roll back when a measure breaches its limit.
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Why the evaluation set needs care
The evaluation set is the part teams most often get wrong. Teams are advised to build from real cases rather than invented ones. Kriv AI's articles recommend treating the set like code: versioned, reviewed and released with approvals, with a record of who labelled what and when.
Where several people label examples, check that they agree with each other, and send uncertain items to a human reviewer. Review the set on a schedule, because the data and the questions people ask drift over time.
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Evaluation versus tracing and monitoring
Evaluation answers whether the system is good. Tracing and monitoring answer what it is doing right now and why. Monitoring tracks known indicators such as errors and delay. Tracing records the steps behind each response, linked by a shared identifier, so you can ask new questions about a failure.
A working setup uses both: evaluation before and at each release, tracing and monitoring after, with live failures flowing back into the evaluation set. The monitoring half starts with understanding what model drift is and how to detect it. The monitoring half can run as a managed MLOps governance service.
kriv approach
How Kriv AI runs evaluations
Kriv AI's published articles recommend: goals and release gates agreed first, a versioned evaluation set built from masked real cases, the evaluation run on every change, and results stored per release. Kriv AI helps teams put this in place.
This is a method, not a promise of any outcome; the right measures and limits depend on the client's workflow.
The detail is in our articles on evaluating Copilot Studio agents, ground truth and human review on Azure AI Foundry, model monitoring and observability on Azure AI Foundry, and the prompt supply chain.
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What this page does not cover
This page explains the framework, not any one tool. It does not rank evaluation libraries and it does not give target scores, because the right numbers depend on the workflow and the cost of a wrong answer. Set them with the people who own the process.
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Straight answers
Frequently asked questions about LLM evaluation framework: what it measures and how a team runs one
What is an LLM evaluation framework?
It is a repeatable way to test a language-model system: an evaluation set with known good outcomes, measures to score it on, and a rule for what blocks a release. It is run before release and after every change.
What does an LLM evaluation measure?
Typically correctness against labelled examples, safety (toxic content, personal-data leaks, policy violations), operations (delay, failures, human hand-offs, cost per request) and output format. Pick them from the job the system does.
How is evaluation different from monitoring?
Evaluation tests whether the system is good against a known set, mostly at release. Monitoring and tracing show what it is doing in production and why, so failures can be traced and added to the evaluation set.
How do you build an evaluation set?
Start from real cases with private data masked, label the expected outcome for each, include edge cases and past failures, version the set, and review it on a schedule.
How often should you run an evaluation?
On every change to a prompt, model, data source or tool setting, and on a regular schedule after release, because inputs and data drift.
Can Kriv AI set up an evaluation framework for us?
Yes. Kriv AI scopes evaluation work after a short discovery call, using the method described on this page. Contact us to start.
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