Checklist · 4 minute read
AI Eval Report Template: Reporting Quality So It Is Believed
Evaluation results that nobody trusts change nothing. A report should state what was tested and how, break results down by category rather than reporting one number, name regressions explicitly, state limitations honestly, and end with a clear recommendation the reader can act on.
Evaluation results that nobody trusts change nothing. This template covers reporting them so they support a decision, drawn from FISTA Solutions' AI enablement delivery work.
What does the report contain?
Six sections, in this order.
| Section | What it establishes |
|---|---|
| Scope and configuration | What was tested |
| Method | How, and by whom |
| Results by category | Where it is strong and weak |
| Regressions | What got worse |
| Limitations | What this does not tell you |
| Recommendation | What to do |
Scope and configuration
Exactly what was tested.
- Model and version tested
- Prompt version tested
- Retrieval configuration and corpus snapshot
- Comparison configuration named
- Date of the run
- Environment used
- Any deviation from the standard suite noted
Method
Enough that a later report is comparable.
- Case count and where cases came from
- Category breakdown of the suite
- Scoring approach: automated, human, or both
- Who scored, and their qualification
- Inter-rater agreement where humans scored
- Any cases excluded, with reasons
- Changes to the method since the last report
Results
By category, because the aggregate hides the decision.
- Overall result stated but not led with
- Results per category
- Comparison against the previous configuration per category
- Statistical caveats where sample sizes are small
- Notable individual failures shown as examples
- Cost and latency reported alongside quality
- Trend across previous runs where available
Regressions
Named individually. See how to set up AI change control.
- Every case that got worse listed
- Category and severity of each regression
- Explanation where one is known
- Assessment of production impact
- Whether each is blocking or acceptable
- Accepted regressions named with who accepted them
- Regressions not averaged into the overall figure
Limitations
Disclosed rather than discovered.
- Coverage gaps in the suite
- Categories with too few cases to conclude
- Known subjectivity in scoring
- Cases from production not yet represented
- Languages or segments not covered
- Adversarial coverage stated
- What this report does not tell the reader
Recommendation
A decision, with reasoning.
- Clear recommendation stated
- Conditions attached where relevant
- Monitoring to apply after deployment specified
- Rollback criteria proposed
- Follow-up work identified
- Named author and reviewer
- Report stored where it can be found later
What are the most common failures?
Leading with an aggregate score. Method omitted so reports cannot be compared. Regressions averaged away. Limitations left out. And a report ending in data with no recommendation.
Who should own this?
The engineer running the evaluation writes it; a domain expert reviews the scoring; the business owner acts on the recommendation. Reports written and accepted by the same person are weaker evidence.
How often should it run?
Per significant change, plus a periodic baseline run so trends are visible independent of changes. Keep the format constant across reports.
What evidence should it produce?
The reports themselves, stored and dated, forming a series. A year of consistent reports is what demonstrates quality is managed.
What if results are poor?
Report them. A report showing a change should not ship is doing its job, and suppressing it means the problem reaches production instead.
The cultural condition for this is that reporting a bad result is not penalised. Where it is, evaluation stops being informative quickly. See why evaluation is the new moat.
What should you do first?
Take your last evaluation result and break it down by category. The aggregate figure almost always conceals something worth knowing.
How FISTA Solutions helps
FISTA Solutions builds and operates production AI systems through AI agents, AI enablement, and forward deployed engineering: evaluation reported by category with regressions named individually and limitations disclosed, ending in a recommendation rather than in data, decisions documented with their reasoning, and handover that leaves your team able to maintain what was delivered. The record is 150+ projects for 50+ companies across 12+ countries.
To adapt this checklist to your environment, message FISTA on WhatsApp, or read how to build an agent evaluation harness.
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Clear answers
Questions raised by this field note.
Straightforward guidance for evaluating scope, fit, and the next step.
01Why break results down by category?
Because an aggregate score can improve while an important category degrades. The breakdown is where the decision-relevant information is, and it is routinely omitted.
02What method details matter?
Which cases, where they came from, how they were scored, who scored them, and what configuration was tested. Without those, a later report cannot be compared against this one.
03How should regressions be reported?
Named individually with the case and the change, not netted against improvements. A change that improves two categories and degrades one is a trade-off, and the reader decides.
04Why state limitations?
Because they exist and stating them builds credibility. Coverage gaps, small sample sizes, and scoring subjectivity are all real and all better disclosed than discovered.
05What recommendation is expected?
Ship, ship with conditions, or do not ship, with the reasoning. A report ending in data leaves the decision to whoever reads it least carefully.
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