AI Evaluation
AI評価セット
AI evaluation tests whether AI outputs or actions meet quality, safety, cost, and business criteria. It is a prerequisite for production generative AI and agent workflows.
What it means
AI evaluation is the practice of repeatedly measuring whether an AI system's answers, decisions, tool calls, and long-task behavior meet expected criteria. It applies not only to classification with known labels, but also to summarization, grounded answers, drafting, code generation, and agent execution. A production evaluation defines test cases, scoring criteria, passing thresholds, human review ownership, and update cadence. Because models, prompts, retrieval data, and tools change, evaluation is an operating system for quality rather than a one-time acceptance check.
How to calculate it
AI evaluation uses scores for each task type and failure rates. Pass rate | Passing cases / eval cases | Shows minimum launch quality Critical failure rate | High-impact errors / eval cases | Captures incident risk Regression rate | Previously passing cases now failing / prior passing cases | Shows change impact
| Lens | Formula / treatment | When to use it |
|---|---|---|
| Pass rate | Passing cases / eval cases | Shows minimum launch quality |
| Critical failure rate | High-impact errors / eval cases | Captures incident risk |
| Regression rate | Previously passing cases now failing / prior passing cases | Shows change impact |
What counts / what does not
AI evaluation covers model, prompt, data, tools, UI, and operating rules. Include | Accuracy, grounding, format, safety, bias, tool actions, review load | Practical quality Exclude | One-off impressions, demo feel, model-name comparisons | Not reproducible Make explicit | Cases, rubric, pass line, reviewer, update cadence | Required for improvement
| Item | Treatment | Why it matters |
|---|---|---|
| Include | Accuracy, grounding, format, safety, bias, tool actions, review load | Practical quality |
| Exclude | One-off impressions, demo feel, model-name comparisons | Not reproducible |
| Make explicit | Cases, rubric, pass line, reviewer, update cadence | Required for improvement |
What moves the number
Evaluation quality depends on representative cases, failure cases, scoring criteria, and regression testing. Case design | Include real user questions and known failures Rubric | Make pass/fail explainable Failure examples | Boundary and risk cases prevent incidents Regression | Verify changes do not break known-good behavior
| Driver | Metric impact |
|---|---|
| Case design | Include real user questions and known failures |
| Rubric | Make pass/fail explainable |
| Failure examples | Boundary and risk cases prevent incidents |
| Regression | Verify changes do not break known-good behavior |
When it helps
Teams can compare model, prompt, retrieval, and fine-tuning options with evidence. Launch gates can block unacceptable failures before production. AI product improvement becomes KPI-driven rather than impression-driven.
- Teams can compare model, prompt, retrieval, and fine-tuning options with evidence.
- Launch gates can block unacceptable failures before production.
- AI product improvement becomes KPI-driven rather than impression-driven.
How to use it
- AI evaluation measures AI output and behavior reproducibly.
- It includes prompts, data, tools, UI, and operations, not only the model.
- Representative, failure, and boundary cases are needed for production confidence.
- Model or prompt changes require regression tests.
- Critical failure rate matters as much as average performance.
Decision cautions
Do not launch on a high average score alone. High-impact use cases need critical-failure gates, not just averages. Easy eval sets create false confidence. Human review should check scorer alignment and rubric drift.
- High-impact use cases need critical-failure gates, not just averages.
- Easy eval sets create false confidence.
- Human review should check scorer alignment and rubric drift.
Read with
AI evaluation is the base layer for generative AI, prompting, tuning, and agents. Generative AI | Produces outputs to evaluate | Needs launch gates Prompt Engineering | Changes need measurement | Keeps iteration grounded AI Agent | Long tasks and tool use need evaluation | Success rate alone is insufficient
| Metric | Role | Why read together |
|---|---|---|
| Generative AI | Produces outputs to evaluate | Needs launch gates |
| Prompt Engineering | Changes need measurement | Keeps iteration grounded |
| AI Agent | Long tasks and tool use need evaluation | Success rate alone is insufficient |
Example
A marketing team uses AI to draft campaign ideas. Initially reviewers judge outputs subjectively, so a model change quietly reduces quality. The team creates an eval set with good and bad prior examples and scores persona fit, brand tone, prohibited phrases, evidence, and CTA clarity. Every prompt change is compared on pass rate and critical failure rate. A brand-violation failure blocks release even if the average score improves. The discussion becomes evidence-based rather than opinion-based.
Compare with
AI Evaluation | Tests outputs and behavior | Judges production quality A/B Test | Compares user response | Measures real-world impact after launch Monitoring | Tracks production behavior | Detects drift after release
| Metric | Difference | Why read together |
|---|---|---|
| AI Evaluation | Tests outputs and behavior | Judges production quality |
| A/B Test | Compares user response | Measures real-world impact after launch |
| Monitoring | Tracks production behavior | Detects drift after release |
Common mistakes
- Evaluation is not one-and-done. Model and data changes require re-evaluation.
- A few human spot checks are not enough without representative and failure cases.
- A high average score can hide unacceptable critical failures.
Frequently asked questions
Is AI evaluation only model comparison?
No. It evaluates prompts, data, retrieval, tools, UI, and operating rules.
How many cases are needed?
It depends on risk. Start with representative and dangerous cases, then expand from usage logs.
Is average score enough?
No. Critical failures and prohibited behavior should be separate launch blockers.