Evaluate AI Tools
How to Evaluate AI Tools for Monitoring & Evaluation
A practical guide to building a reproducible AI tool evaluation protocol using the EvalCommunity AI Tool Evaluation Protocol Builder.
Which AI tool should an M&E team use?
This question is becoming increasingly important as evaluators use AI for qualitative analysis,
survey analysis, data cleaning, indicator classification, reporting, evidence synthesis,
monitoring and other M&E tasks.
But comparing AI tools is more complicated than giving several tools the same prompt and
choosing the answer that looks best.
A good AI tool evaluation should produce evidence—not simply an impressive demonstration.
The AI Tool Evaluation Protocol Builder from EvalCommunity helps M&E
professionals turn an informal AI comparison into a structured evaluation.
You define the evaluation question, establish the benchmark and scoring approach,
compare tools using the same criteria, document errors, complete an integrity checklist,
and generate a reproducible evaluation record.
Why AI Tool Evaluation Needs a Protocol
Imagine that your organization is deciding between three AI tools for qualitative coding.
You give all three the same transcript and ask them to identify themes.
Tool A produces a polished response. Tool B identifies more themes. Tool C provides
fewer results but appears more cautious and flags uncertainty.
Which one is better?
Without a predefined evaluation framework, the answer can easily depend on subjective
impressions.
A protocol establishes the conditions before you evaluate the outputs.
This makes it easier to compare tools consistently and understand what the results actually mean.
The principle
Don’t start with “Which AI tool is best?”
Start with “What task are we evaluating, under what conditions, using what evidence?”
What the AI Tool Evaluation Protocol Builder Does
The tool guides you through five connected stages of an AI evaluation.
Overview
Understand the principles behind a defensible AI comparison.
Define the Evaluation
Specify the task, benchmark, conditions, reviewers and decision rule.
Score the Tools
Apply the same scoring criteria and record errors by type.
Integrity Checklist
Check whether the evaluation conditions are sufficiently controlled.
Evaluation Record
Generate a record describing what was tested and how.
Start With the Evaluation Question
Before selecting or comparing AI tools, define the question the evaluation is supposed
to answer.
The tool asks you to specify an Evaluation Question.
This should describe the decision you want the evaluation to inform—not simply identify
the AI tool you expect to perform well.
Which AI tool provides the most accurate and reliable qualitative coding of interview
transcripts for this evaluation?
A clearly defined question gives the rest of the evaluation a purpose.
Define the M&E Task and Expected Output
Next, describe exactly what the AI tools will be asked to do.
The tool provides a dedicated field for the M&E task and expected output.
Be specific about:
- The input the AI will receive
- The task it must perform
- The expected output
- The format or structure expected
- What constitutes a successful result
Classify 100 indicators against a results-framework taxonomy and provide a category
with a short justification.
Build a Meaningful Benchmark
The benchmark is the set of cases against which the AI tools will be evaluated.
A useful benchmark should not contain only easy examples. Include a mixture of:
- Straightforward cases
- Ambiguous cases
- Difficult cases
- Edge cases
- Negative or problematic cases where appropriate
In the tool, record the benchmark size and describe its composition.
You can also specify the confidentiality level of the benchmark.
If an AI tool performs well only on simple examples, a benchmark containing only
straightforward cases can create a misleading impression of its overall usefulness.
Fix the Prompt and Evaluation Conditions
When comparing tools, the conditions should be as comparable as possible.
The tool allows you to record the standard prompt or task instructions
given to every tool.
If you change the prompt halfway through the comparison because one tool is struggling,
you are no longer conducting the same test.
The tool also allows you to record the number of runs per tool.
This is important because AI outputs can vary between runs.
Also record:
- Model
- Version
- Date tested
- Human reviewers
- Confidentiality conditions
Score Every Tool Using the Same Rubric
Once the evaluation has been defined, move to Score the Tools.
The builder uses six scoring criteria:
Each criterion is scored from 1 to 5. The tool calculates an average
score for each AI tool.
However, the average should not be treated as the complete evaluation.
A numerical average can hide important failures.
Don’t Look Only at the Average Score
One of the most useful features of the builder is the ability to record
errors by type.
The tool provides categories for errors such as:
- Hallucinations
- Omissions
- Incorrect classifications
- Unsupported interpretations
- Fabricated citations
- Calculation errors
- Inconsistent outputs
- Failure to flag uncertainty
- Serious edge-case errors
A high average score does not automatically mean that an AI tool is appropriate
for an M&E task.
Some errors may have substantially greater consequences than others.
Involve Human Reviewers
AI evaluation should not become an exercise in letting one AI system judge another
without human oversight.
The builder includes a field for documenting the human reviewers
involved in scoring the outputs.
Where practical, reviewers can score independently and disagreements can be discussed
or adjudicated using a predefined process.
The goal is to make the evaluation understandable and credible to people who were
not directly involved in selecting the AI tools.
Define the Decision Rule Before Seeing the Results
An evaluation should have a predefined rule explaining how the evidence will inform
the eventual decision.
The builder includes a Decision Rule field specifically for this purpose.
The tool with the highest average score will be considered, subject to review of
serious edge-case errors and other material limitations.
The important point is to establish the logic before the results are known,
rather than changing the evaluation criteria to fit the outcome.
Document Limitations and Check Evaluation Integrity
No AI tool evaluation is perfect. Your conclusion should be limited to the conditions
and task that were actually tested.
Use the Known Limitations field to document issues such as:
- Limited sample size
- Single-language testing
- Specific task conditions
- Unreviewed benchmark cases
- Limited number of runs
- Other constraints affecting interpretation
Then use the tool’s 12-item Integrity Checklist before finalizing
the evaluation.
The AI Evaluation Integrity Checklist
Before relying on the results, check whether:
Generate a Reproducible Evaluation Record
Once the evaluation has been completed, the tool brings the information together
into an Evaluation Record.
The record can capture:
- Evaluation question
- M&E task
- Tools tested
- Models and versions
- Benchmark information
- Standard prompt
- Scoring criteria
- Number of runs
- Error tallies
- Decision rule
- Limitations
- Integrity checklist status
The tool also provides options to copy the record or
print/save it as a PDF.
The evaluation becomes a documented process rather than a one-time demonstration.
Practical M&E Example
Suppose an evaluation team wants to compare three AI tools for
qualitative analysis of 100 interview transcripts.
| Evaluation Component | Example |
|---|---|
| Question | Which tool provides the most reliable qualitative coding? |
| Benchmark | 40 representative test cases |
| Runs | 3 runs per tool |
| Scoring | Accuracy, completeness, relevance, consistency, unsupported claims and uncertainty |
| Error analysis | Hallucinations, omissions, incorrect classifications and other errors |
| Review | Two independent human reviewers |
Instead of saying “Tool A seemed better,” the team can now explain
what was tested, how it was tested, how the tools performed,
what errors occurred, and what limitations apply to the result.
Quick Start: Your First AI Tool Evaluation
- Choose one specific M&E task.
- Write the evaluation question.
- Create a realistic benchmark.
- Decide what the expected output looks like.
- Fix the prompt and testing conditions.
- Decide the scoring rubric before testing.
- Test each tool consistently.
- Record errors separately from the average score.
- Document the model and version.
- Complete the integrity checklist.
- Record limitations.
- Generate and preserve the evaluation record.
Ready to Evaluate Your AI Tools?
Don’t rely on demonstrations or first impressions. Build a structured evaluation
protocol and document the evidence behind your AI tool comparison.
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