To evaluate AI product quality before buying, test each tool on the same frozen set of real tasks with a metric matched to the task. Include enough cases for differences to be real, grade blind with an anchored rubric, and price confident errors separately from abstentions.
Two AI tools score 92% and 88% in your trial. On 200 cases, that gap may be noise, and the weaker tool may prove cheaper.
Nobody would hire a candidate after five questions of their own choosing, marked by gut feel. Many AI trials work in exactly this way. This guide shows you how to evaluate AI product quality as a good exam would: real questions, enough of them, a marking scheme agreed in advance and blind markers. It then turns the results into a cost figure your finance team will accept.
Why AI trials mislead buyers
Most trials fail as measurements. The provider supplies the examples, the team tries a few dozen more, and someone judges that the answers look good. Provider examples are chosen to succeed, and a few dozen cases cannot separate two capable tools. 'Looks good' rewards fluency over accuracy.
The cost appears after signature. Gartner predicted in 2024 that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025, with unclear business value among the reasons. NIST's Generative AI Profile (AI 600-1) names confabulation, meaning confidently stated false content, as a risk specific to these systems.
Match the metric to the task
Quality is not one number. Choose the metric before the trial, because choosing it afterwards invites everyone to favour whatever flatters the preferred tool.
| Task | Failure that matters | Metric to use |
|---|---|---|
| Classifying or routing | Items sent to the wrong place | Precision and recall per category |
| Extracting data | A wrong value in a critical field | Field-level accuracy, critical fields reported separately |
| Answering from your documents | Claims the sources do not support | Groundedness and citation accuracy |
| Summarising or drafting | Missing or invented content | Rubric score for coverage and faithfulness |
| Multi-step agents | Wrong or unapproved actions | Task completion and human interventions |
Two terms deserve a plain definition. Precision is the share of flagged items that were correct. Recall is the share of all correct items the tool found. For contract review, missed risks usually cost more than false alarms, so recall deserves the higher pass mark.
Build a test set large enough to trust
The test set is the most valuable asset of the trial. Build it from your own work rather than public benchmarks, because your documents and edge cases decide your results.
- Source from real work: recent, representative tasks, with sensitive data masked.
- Stratify: include common, hard, edge and out-of-scope cases, and tag each one.
- Write reference answers first: the people who do the work record each expected outcome before any tool sees it.
- Hold back half: keep at least half the set unseen by providers.
- Freeze and version it: reuse it as a regression check after model updates.
Size matters most. For a tool scoring around 90%, the approximate 95% margin of error is about 8 percentage points on 50 cases. It narrows to 4 points on 200 cases and 3 on 400. A 50-case trial cannot reliably tell an 85% tool from a 95% one.
Consider a hypothetical comparison on the same 200 cases. Tool A is right on 184 (92%) and Tool B on 176 (88%). They disagree on 20 cases: A alone is right on 14, B alone on 6. A standard paired test (McNemar's test) gives about 2.5, below the 3.84 needed for significance. The four-point gap is not reliable evidence.
Grade blind, then price the errors
Grading is where bias enters. Strip tool names and shuffle the outputs. Use an anchored rubric, where each score has a written description and an example. Have two graders score an overlapping 20%, and fix the rubric if they agree on fewer than about four items in five.
A model can grade large sets, within limits. A 2023 study found strong model judges agreed with human preferences more than 80% of the time. It also found position bias and a preference for longer answers. Calibrate any model grader against human grading, and never let a provider's model grade its own outputs.
Headline accuracy treats every error alike, but your operation does not. Classify each output as correct, abstained, obviously wrong or confidently wrong. In this hypothetical example, every output receives a one-minute check. An abstention costs eight minutes, an obvious error ten and a confident error 60 minutes of downstream rework.
| Per 1,000 outputs | Tool A | Tool B |
|---|---|---|
| Correct | 920 | 880 |
| Abstained | 0 | 90 (720 min) |
| Obviously wrong | 30 (300 min) | 20 (200 min) |
| Confidently wrong | 50 (3,000 min) | 10 (600 min) |
| Total human time, including checks | 4,300 min | 2,520 min |
Tool A wins on accuracy by four points, yet Tool B needs about 41% less human time. Tool B admits uncertainty instead of guessing. A ceiling on confident errors therefore belongs in every trial.
Agree the pass marks in advance
Agree thresholds with the business owner before the trial, adapted to your task and risk. A tool that misses a critical threshold should not be rescued by strong results elsewhere. Illustrative starting points:
- Task metric: set from your current human error rate.
- Confident error rate: a low ceiling, such as 1 to 2%.
- Human time per output: below the current manual time.
- Grader agreement: about 80% or better before results count.
Apply the same discipline to claims made by others. When a provider quotes an accuracy figure, ask which metric, on how many cases, whose data, graded by whom and how confident errors were counted.
The bottom line
Conclusion
A candidate who answers questions of their own choosing has shown confidence, not competence. An AI trial becomes an evaluation only with the right metric, enough real cases, blind grading and a cost model that penalises confident errors.
This week, pull 50 real tasks from last month's work and have the people who did them record the correct outcome for each. Tag every task as common, hard or edge. That is the first quarter of a test set worth trusting.
Frequently asked questions
How many test cases do I need to evaluate an AI tool?
For a tool near 90% accuracy, 200 cases leave a margin of error of about 4 percentage points and 400 about 3. Smaller sets cannot reliably separate two capable tools.
What is a confident error in AI evaluation?
It is an output that is plausible, stated without hesitation and wrong. It usually costs far more than a refusal, because it is caught late, if at all.
Is it safe to use an AI model to grade AI outputs?
It can work for large test sets if the model grader is first calibrated against human grading, outputs appear in random order, and no provider's model grades its own outputs.
Should I use public AI benchmarks to compare tools?
Public benchmarks show general capability. Quality in practice depends on your own documents and requests, so a test set built from your work is the better guide.
What is the difference between precision and recall?
Precision is the share of items the tool flagged that were correct. Recall is the share of all correct items that the tool found.
References
- Gartner, 30% of generative AI projects will be abandoned after proof of concept by end of 2025 (29 July 2024)
- NIST, AI 600-1 Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (26 July 2024)
- arXiv, Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena (June 2023, revised December 2023)
Disclaimer: General information only, not legal, security, compliance or financial advice. Check the cited sources and seek professional advice. Names belong to their owners; no endorsement implied.