AI generated an answer ≠ AI generated a correct answer. Evaluation turns that gap into evidence we can use to improve or release a system.
AI Evaluation is the structured process of checking whether an AI model or AI-powered system does what people need, at an acceptable level of quality, cost, speed, stability, and safety.
A fluent answer can still be wrong, unsupported, incomplete, unsafe, too slow, or too expensive. Evaluation tests the behavior that matters for the real task.
Compare a model's capability on a defined task.
Example:Can it classify an invoice correctly?
Check language outputs for quality and behavior.
Example:Is the answer useful, complete, and safe?
Evaluate both retrieval and the generated answer.
Example:Did it use the right policy paragraph and cite it?
Check multi-step decisions, tools, and outcomes.
Example:Did the coding agent change the right file and pass tests?
Metrics are signals, not the whole decision. Pick metrics that match the job and review representative examples too.
How often is the answer correct?
Example: 9 of 10 invoice totals match the source.Does it answer this question rather than a nearby one?
Example: a leave-policy answer addresses annual leave, not sick leave.Does the answer stay consistent with the supplied context?
Example: it does not add a benefit missing from the handbook.Can important claims be tied to trusted evidence?
Example: each policy claim points to a retrieved source.Did it include the necessary parts of the answer?
Example: a deployment checklist includes rollback and owner.Does it avoid harmful, disallowed, or risky behavior?
Example: it refuses an unsafe action and offers a safe next step.How long does the user wait?
Example: 95% of support answers arrive within 3 seconds.How much does each task or successful outcome cost?
Example: a workflow uses fewer tokens without losing quality.How often do tool calls complete the intended action?
Example: the agent opens the correct pull request and records the result.People score or compare outputs using a rubric.
Good for: nuance, usefulness, tone, and edge cases.
Rules check exact conditions.
Example: JSON is valid, a required field exists, or a refusal phrase appears.
A standard dataset or test suite makes comparisons repeatable.
Limit: a benchmark may not represent your product.
Another language model scores an output against a rubric.
Guardrail: calibrate it against human judgments.
A pipeline runs test cases, calculates metrics, and reports regressions.
Best use: frequent checks in development and deployment.
A user asks: “How many annual leave days do I get?”
A coding agent receives a bug report and edits a repository.
Do not ask only: “Which model is the smartest?” Ask which system produces the best business outcome under real constraints.
Does the result help the user complete the job?
Can the workflow operate within budget?
Is the experience fast enough for the workflow?
Does performance hold across releases and edge cases?
Are risks controlled when the system is wrong?
Does it improve the outcome the product exists to deliver?
A single score can hide cost, latency, safety, or failures on important cases.
Benchmarks help compare systems, but product evaluation must reflect your users and workflow.
Evaluation reduces uncertainty and exposes risk; it does not guarantee every future output.
| ENGLISH TERM | 中文 | SIMPLE ENGLISH | 中文小白理解 |
|---|---|---|---|
| Evaluation | 评估 | Measuring whether an AI system meets expectations. | 检查 AI 系统是否达到预期。 |
| Metric | 指标 | A signal used to measure one dimension of performance. | 用来衡量某一方面表现的信号。 |
| Faithfulness | 忠实性 | Whether an answer stays consistent with supplied context. | 回答是否忠于提供的资料。 |
| Groundedness | 有依据性 | Whether claims connect to trusted evidence. | 回答是否能在可信证据中找到依据。 |
| LLM-as-a-Judge | 用大语言模型评审 | An LLM scores another output using a rubric. | 让一个模型按标准给另一个输出打分。 |
| Regression | 回归问题 | A previously working behavior becomes worse after a change. | 改动后原来正常的功能变差了。 |
| Tool Success Rate | 工具成功率 | How often tool calls achieve their intended result. | 工具调用成功完成目标的比例。 |
AI evaluation asks whether the system is correct, useful, grounded, complete, safe, fast, affordable, stable, and successful for the real job—not merely whether it sounds intelligent.