What Does an AI Evaluator Do? A Beginner’s Guide

An AI evaluator examines an AI response and explains how well it meets the task. The central skill is making a judgment that another person can check: identify the requirement, point to the evidence, and explain the effect of any mistake.

You might notice that an answer sounds helpful but changes a deadline. You might prefer a shorter answer because it follows the request more closely. You might find that neither answer deserves a high rating. In each case, the useful contribution is the reason behind your decision.

This guide focuses on human review of written responses. AI evaluation also includes technical testing, but you can practise the human judgment skills here with a prompt, an answer, and a clear set of criteria.

Where human judgment fits

In the InstructGPT research, people wrote demonstration answers and ranked model outputs; those judgments helped train models. This is one concrete example of human evaluation contributing to model improvement. Read the original InstructGPT paper.

For a beginner, that example helps distinguish two activities. Producing an answer shows what a good response could look like. Evaluating an answer explains whether the response already given meets the requirements. A practice session may involve both, but do the evaluation before rewriting: otherwise, you can lose track of the original error.

The following task formats are useful ways to prepare. A particular assignment may use different labels, tools, or scoring rules.

What to look for in an answer

Start with the user's goal. If someone asks for a short announcement, a detailed essay may be informative but unsuitable. If they ask for a summary based only on a supplied note, additional facts need scrutiny even when they sound plausible.

For practice, separate four questions:

1. Instructions: Did the answer obey the requested format, scope, audience, and restrictions?
2. Accuracy: Are its statements supported by the available evidence?
3. Reasoning: Does the conclusion follow from the information given?
4. Usefulness: Can this particular user act on the answer without avoidable confusion?

Treat these as a starter framework, then follow the rubric supplied with the task. A rubric is the set of criteria used to make the judgment. Do not silently replace it with your own preferences.

A 2021 study of identifying machine-written text found that reviewers could focus on misleading surface features. The authors recommended checking evaluators' reasons against the intended criteria. Read the human-evaluation study.

That study examined authorship detection. Our practical takeaway for answer review is narrower: polished writing should not substitute for checking the actual task.

A fictional worked example

Imagine a community workshop with this information sheet:

Registration closes on 18 November. The workshop starts at 10 AM on 23 November. Bring a notebook. Equipment is provided. The information sheet does not mention refreshments.

User request: “Write a two-sentence reminder. Include the registration deadline and what attendees should bring. Use only the information sheet.”

AI response:

Register by 23 November for our 10 AM workshop. Bring your notebook and enjoy the free refreshments provided.

An impression such as “This is friendly and concise” misses the important problems. Evaluate each requirement instead.

The response has two sentences, so it meets the requested sentence count. It includes the notebook, so that detail is correct. However, it presents the workshop date as the registration deadline. It also adds free refreshments, which the information sheet does not support.

A useful evaluation note would be:

The response meets the two-sentence format and correctly asks attendees to bring a notebook. It gives the wrong registration deadline: 23 November is the workshop date, while registration closes on 18 November. The refreshments claim is unsupported by the supplied sheet. These errors could mislead attendees about registration and what is provided.

The note records successes as well as failures. It names the evidence and explains the consequence without speculating about why the AI made the error.

If the task also requests a correction, an improved version is:

Register by 18 November for the workshop on 23 November at 10 AM. Please bring a notebook; equipment is provided.

The correction stays within two sentences and removes the unsupported promise. It repairs the response against the original request rather than adding unrelated improvements.

Try a short evaluation yourself

Fictional task: A library notice says, “The reading room is closed on Monday. Book returns remain available through the outdoor return box.” The user asks for one sentence mentioning both facts, without adding opening hours.

AI answer: “The reading room is closed on Monday, but you can return books through the outdoor box until 6 PM.”

Before reading the answer key, write one sentence identifying what passes and what fails.

Answer key: The response uses one sentence and includes both required facts, but adds an unsupported 6 PM cutoff. A precise note is: “The answer includes the closure and return option, but the notice gives no return-box hours, so the 6 PM limit should be removed.”

Only a few words were added, but they change what the reader might do. Your judgment should reflect the requirement they violate.

Common beginner mistakes

Starting with a score. First collect evidence, then apply the rubric. A score without a reason is difficult to review.

Rewarding confidence. A firm tone does not establish that a claim is true. Check dates, quantities, conditions, and exceptions carefully.

Adding requirements. You may prefer bullet points, but a paragraph is acceptable when the prompt permits it. Evaluate the task that was asked.

Treating uncertainty as failure. When the supplied material does not settle a claim, record that limitation. Do not invent missing evidence to force a confident verdict.

Writing vague feedback. Replace “The answer is inaccurate” with the exact statement, the conflicting evidence, and the effect on the user.

Build a preparation habit

Practise with one short fictional task at a time. Write the requirements before reading the response. Record one strength, one issue if present, and an evidence-based explanation. Revisit your judgment later and check whether the same criteria still support it.

When you are ready to apply that habit, take your first AtomicEval challenge. It has a five-minute target and can be started without signing up; a free profile is required to view and save your result.

You can also create a free AtomicEval profile to start your preparation.

Sources and further reading

• Training language models to follow instructions with human feedback, Ouyang et al., 2022.
• All That's ‘Human’ Is Not Gold: Evaluating Human Evaluation of Generated Text, Clark et al., 2021.