How do I evaluate an AI model?

Start with a small set of real tasks from your work. Define evaluation criteria before running the test, use the same prompt for each model, and score each criterion separately. Note the model version and date so results can be compared when models update.

What criteria should I use to evaluate AI models?

Common criteria for knowledge-work tasks are factual accuracy, how much correction the output needed, style fit, completeness, and speed. Define what a good result looks like before you see any outputs so the scoring is consistent.

How often should I re-evaluate AI models?

A quarterly check on your most important task categories is a reasonable starting point for most teams. Model vendors update capabilities regularly, and the relative performance of models can shift. Set a schedule and record it as a decision so it does not depend on someone remembering.

Should I use benchmarks when evaluating AI models?

Published benchmarks are useful background. They show how a model performed on standardized tests and give you a rough sense of capability. For a practical work decision, they need to be combined with tests on your own tasks, because benchmark performance does not always translate directly to your specific use case.

How do I keep model evaluation results so my team can use them?

Record the decision, the reasoning, and the test date in a place the whole team can find. If you use a shared tool like fenbs, record it as a Decision with a standing rule. That way connected AI assistants also see the decision at the start of each session.

AI Model Evaluation: Test Models on Your Actual Work

A good AI model evaluation uses your real tasks, not just published benchmarks. This guide walks through a simple process for comparing models and recording what you find.

September 24, 2026Updated October 21, 20264 min read

AI model evaluation means testing a model against the work you actually need it to do. Published benchmarks are a starting point, but they measure performance on standardized test sets that may not reflect your tasks, your domain, or the way you write prompts. A small evaluation using real tasks gives you better evidence for a decision. Tools like fenbs let you record evaluation decisions as standing rules, so connected assistants know which model to use for which task without needing you to re-explain the choice.

The evaluation does not need to be elaborate. A consistent process applied to a small set of representative tasks is more useful than an exhaustive but one-time test.

Choose your evaluation tasks first

Start with three to five tasks that represent your real work. Be specific. "Write a paragraph" is not useful; "write a 150-word executive summary of this financial report for a non-specialist reader" is. The more the task matches what you actually need, the more the evaluation result will matter.

Choose tasks across different categories if your work spans them: one writing task, one analysis task, one coding task. A model that scores well on writing may score lower on code review. You want to know the shape of the trade-off, not just a single number.

Set your criteria before you run the test

Define what a good output looks like before you see any outputs. Common criteria for knowledge-work tasks include: factual accuracy, need for correction (how many edits did the output require), style fit (does it match your voice or format), completeness, and speed.

Use a simple scoring scale — three or five points is enough. Avoid single-question summaries like "was this good?" because they are hard to compare across models and hard to act on. A criterion-by-criterion score gives you a clearer picture of where each model is stronger.

Run the evaluation consistently

Use the same prompt for each model. Do not edit the prompt between runs. If one model needed a follow-up prompt to produce a usable answer, note that as part of the evaluation, rather than improving the prompt and re-running.

Note the model name, the version, and the date. Models update regularly. A comparison that favored one model in April may no longer reflect the current versions in October. Version and date give you a baseline you can return to when a model is updated.

If you are comparing models across multiple task categories, try to run each task category in a single session to keep conditions consistent. Responses can vary based on time of day, temperature settings, and system prompt differences that are easy to overlook.

Record observations, not just scores

A numeric score is useful for comparison but not for future decisions. Also write a short note on why each score was given: what the model did well, where it fell short, and what you had to fix. That note is what a colleague or a future session needs when they ask why a particular model was chosen.

Keep the observations attached to the task they describe, not in a separate document that will be hard to find later. The decision you reach — "use Claude for long contract analysis, GPT-4o for first-draft writing" — should be recorded with the evidence that supports it.

How fenbs helps

Turn evaluation results into standing rules

Record each model decision in fenbs as a Decision with the task type, the chosen model, the reasons, and the test date. Add a re-test condition if the model landscape changes. Every connected AI app reads those rules first, so the decision holds across sessions without anyone repeating it.

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What to do with the results

After scoring, look for patterns rather than a single winner. One model may be stronger on writing and weaker on structured analysis. A mixed strategy — different models for different task types — is often more efficient than searching for a single model that beats the others everywhere.

Set a re-evaluation schedule. Models update regularly and relative performance shifts. A quarterly check on your most important task categories is usually enough for most teams. Record the schedule as a decision so it does not depend on someone remembering to do it.

How to do this with fenbs

Create a free account on fenbs. Add a project for your model evaluation with the scope and criteria written into the description.
Create a task for each test prompt. Write the prompt and the scoring criteria in the task plan.
Run the evaluation and add your observations and scores as comments on the task.
When you reach a decision, record it in Decisions and rules with the model name, task type, reasons, and test date.
Connect your chosen AI apps from /integrations. Ask each assistant to read the project context before starting work; it will see the current model decisions without you re-explaining them.

When fenbs is not what you need

fenbs is for recording the decisions that come out of an evaluation, not for running automated evaluations at scale. If you need a pipeline that tests hundreds of prompts programmatically and scores outputs with a metric function, that is a developer evaluation framework. Each model vendor publishes tooling for that kind of evaluation. fenbs fits when the result you need to preserve is the human decision about which model to use, not the raw test data itself.