Evaluating AI output
Practical ways to check whether AI answers are accurate, useful and safe before you rely on them.
What is it?
Evaluating AI output means checking results against what you actually need: correct facts, complete answers, the right format, appropriate tone and no harmful content. For one-off use, this can be a quick personal review. For apps and repeated tasks, it means building a set of test cases and scoring results in a consistent way.
Teams often combine human review with automatic checks, such as format validation, comparison with known correct answers, and using another model as a grader with clear criteria. No single check catches everything.
Which models use it?
- AI developers such as OpenAI, Anthropic and Google publish evaluation results and guidance for their models.
- Open-source tools and libraries help teams run repeatable evaluations of prompts and models.
- Many AI apps include thumbs up/down feedback to collect user ratings.
- Using one model to grade another's output against a rubric is a common technique.
How to use it
- Define what a good answer looks like for your task, in writing.
- Build a test set of real examples, including tricky and edge cases.
- Check facts, numbers and quotes against trusted sources.
- Score each answer against your criteria, by hand or with an automated grader you have checked.
- Track results when you change prompts or models.
- Keep a human review step for high-stakes outputs.
Benefits
- Catches errors before they reach users or decisions.
- Shows objectively whether a change improved things.
- Builds justified trust in where AI can and cannot be used.
- Helps choose the right model and prompt for the job.
Limits and risks
- Building good test sets takes time and domain knowledge.
- Automated graders can also be wrong or biased.
- Passing tests does not guarantee good behaviour on every new input.
- Some qualities, like helpfulness or tone, are partly subjective.
Prompt template
Copy it, replace the parts in [brackets] and paste it into any AI chat.
Review the AI-generated text below as a strict fact-checker and editor. For each claim, label it Verified (common knowledge), Needs source, or Likely wrong, and explain briefly. Then score the text from 1 to 5 on accuracy, completeness, clarity and tone for [audience], and list the top three fixes. Do not add new facts you are not sure about. Text: [paste AI output]
More in Building with AI
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- AI agents and tool useAI systems that can take actions, such as searching, running code or using apps, to finish a task.
- MCP (Model Context Protocol) and connectorsAn open standard for connecting AI assistants to tools and data sources in a consistent way.
- RAG (retrieval-augmented generation)Letting an AI answer from your own documents by finding relevant passages first.
- Embeddings and semantic searchTurning text into numbers that capture meaning, so you can search by idea rather than exact words.
- Fine-tuning vs promptingWhen to customize a model with extra training, and when better prompts are enough.