AIAIGuide
AI Project Evaluation: Metrics Beyond a Good Demo
A convincing demo shows that an AI system can work, not that it works reliably on your data, users and edge cases. This guide covers evaluation sets, task metrics, business outcomes, cost and risk, and how to decide whether to scale.
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- Updated
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- 3 minutes
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- EngineSpire Technologies
Key points
- Define success criteria and a baseline before the pilot starts.
- Build evaluation sets from real, representative data, including hard and rare cases.
- Choose metrics that match the task and the different costs of different errors.
- Measure business outcomes, adoption, cost and risk, not model scores alone.
- Keep evaluating after launch, because data, users and models change.
Why demos mislead
- Examples are chosen because they work.
- Inputs are clean, short and in one language.
- Edge cases, integrations and volumes are left out.
- Novelty makes early users forgiving.
Before the pilot
- State the decision the evaluation will inform: scale, change or stop.
- Measure the current process as a baseline: accuracy, time, cost and customer impact.
- Agree success criteria and thresholds with sponsors and the people who will use the system.
- Decide how long the pilot runs and how many cases it must cover.
Build a representative evaluation set
- Sample real inputs from production, across branches, customer types and time periods.
- Include difficult cases: poor-quality scans, incomplete forms, unusual requests and mixed-language messages, such as English, Filipino and Taglish, if your customers write that way.
- Have experts record the correct outcomes, and check that they agree with each other.
- Keep part of the set aside and never use it for tuning.
- Handle personal data in the evaluation set with the same care as in production.
Metrics that match the task
| Task | Useful metrics |
|---|---|
| Classification and detection | Precision, recall and their balance, broken down by class |
| Data extraction | Field-level accuracy and the share of items processed without manual work |
| Forecasting | Average error and bias compared with the current forecasting method |
| Search and retrieval | Whether relevant items appear among the top results |
| Generated text | Correctness, completeness, groundedness in sources, format compliance and harmful content rate, scored with a rubric |
| Conversational assistants | Resolution rate, escalation rate and user satisfaction |
Errors do not cost the same
A false alarm in fraud detection costs an analyst's time; a missed fraud costs money. Precision is the share of flagged items that were correct; recall is the share of relevant items that were found. Improving one usually lowers the other, so set thresholds based on the real cost of each type of error.
Evaluating generated text
- Write a rubric with clear criteria and examples of good and poor answers.
- Have domain experts score a sample.
- Automated grading with a language model can speed things up, but calibrate it against human scores and keep checking.
- Test deliberately for prompt injection, unsafe content and confidential data leaks.
Business outcomes
- Time saved per case and throughput.
- Reduction in errors and rework.
- Customer response and resolution times.
- Where possible, compare against a control group or a before-and-after period.
Operational measures
- Response time and availability.
- Cost per transaction, including model usage, licences and infrastructure.
- Human review workload and exception rates.
Risk and fairness
Check performance across segments such as regions, languages and customer types, not just overall. Document failure modes, test security weaknesses and confirm privacy compliance. The measure and manage functions of the NIST AI Risk Management Framework provide a structure for this work.
Go, change or stop
| Question | Evidence |
|---|---|
| Does it meet the agreed quality thresholds? | Evaluation set results, broken down by segment |
| Does it improve the business outcome? | Comparison with the baseline |
| Is the cost sustainable at full volume? | Cost per transaction projected to scale |
| Are the risks acceptable and controlled? | Risk assessment, security testing and privacy review |
| Will people use it? | Adoption and feedback from pilot users |
After launch
- Monitor quality indicators and sample outputs for review.
- Watch for drift as inputs, products or customer behavior change.
- Re-run the evaluation set whenever the model, prompts or provider change.
- Version models, prompts and evaluation data together.
Common questions
What is the difference between precision and recall?
Precision is the share of items the system flagged that were correct. Recall is the share of all relevant items that it found. Improving one often lowers the other, so the right balance depends on which errors cost more.
How big should an evaluation set be?
Large enough to include the variety of real inputs, including rare but important cases, and to give stable results for the metrics that matter. Start with what experts can label well and grow the set as you learn where the system fails.
Can we use AI to evaluate AI output?
Automated grading with a language model can speed up evaluation of generated text, but it needs calibration against human judgments and regular human review, because automated judges have their own errors and biases.
Sources and further reading
- [1]Classification: Accuracy, recall, precision, and related metrics — Google for Developers
- [2]Rules of Machine Learning — Google for Developers
- [3]AI Risk Management Framework — NIST
Need this applied to your environment?
We can turn this guide into a scoped assessment of your own sites, systems and constraints.
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