AI Evaluation (Eval)

AI evaluation is the systematic testing of an AI system against defined examples and criteria to determine whether it performs its intended task reliably enough.

AI evaluation is the systematic process of testing whether an AI system performs a defined task well enough for its intended use.

Evaluations are often shortened to evals.

What an Evaluation Can Measure

An evaluation might measure:

Classification accuracy
Factual correctness
Retrieval quality
Formatting
Tool selection
Citation quality
Policy compliance
Hallucination rate
Response usefulness
Latency
Cost
Human preference

The important metric depends on the use case.

Evaluation vs. Demo

A demonstration answers:

Can the system produce a good result?

An evaluation asks:

How reliably does it produce acceptable results across representative cases?

That is a much higher standard.

Evaluation Dataset

A useful evaluation usually needs a set of examples representing real expected inputs.

For example:

Input:
"I was charged twice."

Expected category:
Billing

Model output:
Billing

Result:
Pass

Repeated across many realistic cases, these examples can reveal patterns that a few manual tests miss.

OpenAI's evaluation tooling similarly defines an evaluation around testing criteria and a dataset so performance can be compared across model or configuration changes. OpenAI Evals API reference

Why It Matters

AI behaviour can change when you change:

AI Model
Prompt
Retrieval logic
Source documents
Tool descriptions
Fine-Tuning
Context construction

Without evaluation, teams may discover regressions only after users encounter them.

Evaluating RAG

A Retrieval-Augmented Generation system may need at least two separate questions:

Retrieval quality

Did we find the correct source information?

Answer quality

Did the model use that information correctly?

A correct answer produced from the wrong source can hide a fragile retrieval system.

Human Evaluation

Not everything can be measured automatically.

Human reviewers may be useful for qualities such as:

Usefulness
Tone
Nuance
Source interpretation
Ambiguous edge cases

That can be combined with automated evaluation.

Common Problems

Common problems include:

Testing only easy examples
Evaluation set copied from training examples
Metrics unrelated to business requirements
No baseline
Changing several things at once
Evaluating model output but not Tool Use
Treating one aggregate score as the entire story
No evaluation after production changes

How to Improve

Start with the failure that matters.

Define:

Desired behaviour
Representative examples
Pass/fail criteria
Important edge cases
Baseline performance
Human-review requirements
Regression tests

Then run the evaluation whenever important system components change.

This is the AI equivalent of making changes measurable rather than trusting that they “seem better.”