How to Evaluate AI Agents and Prompts
Quick Answer
You can evaluate AI agents and prompts by defining expected behavior, creating test cases, scoring outputs, checking for hallucinations, testing edge cases, and rerunning regression tests after changes. A good evaluation separates proposed tests from completed results and never invents pass rates or benchmark scores.
Why AI Evaluation Matters
An AI agent or prompt that works well on the first few examples you try can still fail on inputs you haven't tested, especially edge cases, unusual phrasing, or adversarial inputs. Evaluation turns "it seemed to work" into a structured process that tells you where it actually holds up and where it doesn't.
Define Expected Behavior First
Before testing, write down what the agent or prompt is supposed to do, including what a correct response looks like and what it should refuse to do. Without a clear definition of success, you can't tell whether a given output is actually good or just plausible-sounding.
Create Test Cases
Build a set of test cases covering typical inputs, edge cases, and inputs designed to probe for failure, such as ambiguous requests, missing information, or attempts to get the agent to do something outside its intended scope. Include cases with a known correct answer so you can check outputs objectively where possible.
Score Outputs With a Rubric
Define a simple rubric for judging output quality: accuracy, completeness, tone, safety, and adherence to instructions are common dimensions. Score actual outputs against actual test cases; never report a score or pass rate without having actually run the test.
Test for Hallucinations
Specifically test whether the agent invents facts, sources, or details when it doesn't have enough information to answer confidently. A well-designed agent should say it doesn't know or ask for clarification rather than confidently stating something false.
Test Safety and Edge Cases
Test how the agent handles inputs that are outside its intended use, ambiguous, or potentially harmful. Confirm it responds appropriately, whether that means declining, asking for clarification, or handling the edge case gracefully rather than breaking or producing an unsafe response.
Create Regression Tests
Whenever you update a prompt or agent, rerun your existing test cases to confirm the change didn't break something that used to work. This is especially important for changes that seem unrelated to the part you're testing, since prompt changes can have unexpected ripple effects.
AI Evaluation Prompt Template
"Here's what this agent/prompt is supposed to do: [description]. Here are test cases to run: [list]. For each one, evaluate the actual output against these criteria: [criteria]. Flag any hallucinations, safety issues, or failures to follow instructions."
Final Checklist
- Expected behavior is clearly defined before testing begins
- Test cases cover typical inputs, edge cases, and adversarial inputs
- Scores and pass rates are based on actually running the tests, not estimated
- Hallucination and safety behavior have been specifically tested
- Regression tests are rerun after any change to the prompt or agent
Related PiSkill Resources
Use the Code Debugger & Error Fixer Prompt when an agent's output reveals a logic issue, and the Business Idea Validator Prompt if you're evaluating an AI-powered product concept.