AI Evaluation and Guardrails
Learn how to evaluate AI outputs and design guardrails that reduce quality, safety, and product risk.
8 min
AI evaluation answers a practical question: is the system good enough for this workflow, and how will we know when it gets worse?
Evaluation Types
| Type | Question |
|---|---|
| Golden examples | Does the system handle known cases? |
| Retrieval eval | Did it fetch the right evidence? |
| Generation eval | Is the answer accurate and useful? |
| Safety eval | Does it avoid disallowed behavior? |
| UX eval | Does the response help the user complete the job? |
Scoring Criteria
Use criteria that map to product outcomes. Accuracy, completeness, groundedness, citation quality, tone, safety, latency, and cost may all matter.
type EvaluationScore = {
accuracy: 1 | 2 | 3 | 4 | 5;
grounded: boolean;
missingInformation: string[];
reviewerNotes: string;
};
Guardrails
Guardrails are controls around the model. They include input validation, permissions, content policies, output schemas, source requirements, confidence thresholds, and human approval.
A guardrail is strongest when it is enforced by software, not just requested in a prompt.
Human Review
Human review is useful for high-impact or uncertain actions. Design the review experience so reviewers can see the model output, evidence, tool calls, and reason for escalation.
Next Step
Add three evaluation cases to one quiz or project, then compare how a prompt change affects the scores.
Practice this topic
Reinforce the concepts from this lesson with a short quiz and explanation review.
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