An AI demo can look convincing after five carefully chosen examples. A business workflow has to cope with the sixth example, the incomplete attachment, the unusual request, the ambiguous instruction, and the ordinary case entered in an unexpected format.
That is why AI needs evaluation, often shortened to evals. An eval is a repeatable way to test whether an AI-supported task performs well enough for its intended use. It replaces a general impression with evidence the team can inspect.
Evals do not prove that a system will never fail. They help you understand how it behaves, decide what is acceptable, compare changes, and place human review where it is needed.
Start with a task, not a general impression
“Evaluate our AI assistant” is too broad. A useful evaluation focuses on a defined job.
Examples include:
- extract specific fields from incoming documents;
- classify a request into an agreed queue;
- draft a response using approved source material;
- summarize a case for human review;
- identify records that may need closer attention.
For each task, write down the input, expected output, allowed sources, intended user, and action that follows. The same model may be suitable for a low-risk internal summary and unsuitable for a customer-facing decision. Context changes the standard.
Also name what the AI must not do. It may not invent missing information, expose confidential data, make a final eligibility decision, or send a message without approval. A useful evaluation includes these boundaries.
Define what a good result means
Quality is rarely one number. Break it into dimensions that match the work.
Correctness
Does the output match the known facts or expected label? For extraction, compare each required field with a verified answer. For classification, check whether the item went to the right category.
Completeness
Did the output include all required elements? A polished summary that omits the one fact needed for a decision is not a good result.
Grounding
Can the output be supported by the permitted source material? If the AI should answer only from an approved policy, it must not fill gaps with plausible general knowledge.
Format and usability
Can the next person or system use the result? Check the required structure and length. Confirm the tone, citations, and visibility of uncertainty.
Safety and boundaries
Does the system refuse, escalate, or limit itself when it encounters sensitive, prohibited, or unsupported requests? Test the boundaries directly.
Operational effect
What happens after the output? A small classification error matters differently if it creates a review task than if it automatically rejects a request. Evaluate the workflow consequence, not only the text on screen.
Create a short scoring guide for each dimension. Use examples across the scoring range so reviewers apply the standard consistently.
Build a representative evaluation set
An evaluation set collects inputs with expected results or scoring guidance. It should reflect actual work.
Include:
- routine cases from normal use;
- messy formats, vague language, and missing or conflicting details;
- less frequent exceptions with meaningful consequences;
- requests the system should decline or escalate;
- attempts to override instructions or obtain restricted information.
Use real examples only when you have an appropriate basis, access controls, and a safe way to remove or protect sensitive information. Otherwise, create clearly labelled test cases based on documented patterns. Do not paste confidential business material into an unapproved public tool.
Include enough variation to reveal different failure modes. Do not let a rare edge case dominate unless its consequence justifies that weight.
Establish a baseline
Before comparing models or changing instructions, record how the current process performs.
The baseline might be human performance, an existing rules-based workflow, the current AI configuration, or a simple manual method. It gives the team something concrete to compare.
Record quality and operating measures separately. A new approach may improve drafting time while increasing review effort. Without a baseline, a visible gain can be mistaken for overall improvement.
Run evaluations consistently
Use the same evaluation set and configuration when comparing changes, with consistent scoring rules. Record the model or service version, instructions, connected sources, tool permissions, and date. AI providers and surrounding systems can change, so reproducibility matters.
Some checks can be automated. Required fields, valid formats, known labels, or exact source references are suitable candidates. Other qualities need human judgment. Tone, usefulness, ambiguity, and the seriousness of an unsupported claim often need a reviewer who understands the work.
Where possible, hide which version produced each output during review. If experienced reviewers disagree, the task or scoring guide may be unclear.
Set release rules before seeing the result
Decide what must be true before the AI enters production. This avoids lowering the standard because a demo looks promising.
A release rule can include:
- no critical boundary failures in the test set;
- acceptable performance on required quality dimensions;
- human review for defined cases;
- a clear escalation route for uncertainty;
- permissions limited to the task;
- logging and monitoring in place;
- a tested pause and fallback process.
Not every error has equal weight. A formatting issue may be inconvenient. An invented policy statement, privacy breach, or unreviewed consequential action may be a stop condition. Make that distinction explicit.
Keep evaluating after launch
Production creates new evidence. Monitor corrections, overrides, complaints, unusual inputs, review time, and cases the system could not handle. Add important failures to the evaluation set after removing or protecting sensitive details.
Retest when the model, instructions, connected data, tools, policy, or workflow changes. Also retest when the type of incoming work shifts. A system can remain technically unchanged while its operating environment moves around it.
Give users an easy way to flag a questionable output. Their feedback should reach an owner who can investigate and decide whether to update the system, the process, the guidance, or the review rule.
A practical eval plan
Before relying on AI in a workflow, answer:
- What exact task are we testing?
- What sources and actions are allowed?
- What does acceptable mean for quality, safety, and usability?
- Does the test set represent routine work, exceptions, and boundaries?
- What is the current baseline?
- Which checks can be automated, and which need an informed reviewer?
- What failures stop release?
- Who owns monitoring and retesting?
If those answers are unclear, the team is not ready to judge the AI, even if the demo is impressive.
What to take away
AI earns a place in a business workflow through evidence. Define the task, build a representative test set, score what matters, and connect every quality judgment to the action that follows. Then keep evaluating as the system and the work change.
The result is an honest view of capability, limits, and the oversight required.
Put one AI-supported workflow to the test
Start with the task, the evidence available, and the consequence of a wrong result.
