A property management firm in Bristol used an AI tool to handle first-pass responses to maintenance requests. The team thought it was doing well. After three months, a client pointed out that the tool had been directing tenants to a phone number changed six months earlier. Nobody had tested whether the responses were still accurate after the update.
That kind of situation is not unusual. When you deploy an AI tool without a structured way of checking its outputs, you rely on luck and informal spot-checks. For low-stakes internal drafting, that might be acceptable. For anything customer-facing, compliance-adjacent, or high-volume, luck is a poor substitute for a repeatable check.
That is where an AI eval comes in.
What is an AI eval?
An AI eval, short for evaluation, is a structured test that checks whether an AI system’s outputs meet the standard you have set for a specific task. You define a set of inputs, describe what a good output looks like across several criteria, and score the system’s actual responses against that description. The result tells you whether the system is working, and where it is not.
An eval set normally includes a mix of typical inputs, edge cases, and known failure modes. Braintrust, a platform that builds evaluation tooling, recommends starting with five to ten examples representing the typical customer or user interaction, then expanding the set as real production issues emerge. Mind the Product describes a benchmark range of 50 to 100 diverse queries for more established setups, though for a small services firm, the starting five to ten examples are enough to get meaningful signal.
The scoring rubric is what makes an eval useful rather than arbitrary. A well-designed rubric defines what a score of one looks like, what a three looks like, and what a five looks like, so that two different reviewers applying the same rubric reach broadly the same score. That consistency is what lets you compare runs over time and spot genuine regressions when something changes.
Why does it matter for your business?
Without an eval, the only way to judge your AI tool’s outputs is through occasional spot-checks and the absence of complaints. That works until it doesn’t. A structured eval replaces informal judgment with a repeatable process you can run before any significant change, and one you can point to if a client, regulator, or auditor asks how you are checking the quality of AI-generated content.
The UK Information Commissioner’s Office published AI and data protection guidance in 2024 that centres on accountability, accuracy, and human oversight in AI systems handling personal data. The FCA has set out governance expectations for AI use in financial services, covering explainability, bias, and consumer protection. Neither document mandates a formal eval by that name, but both create a governance expectation that a practical eval suite directly addresses.
The legal AI company Harvey developed a task-specific evaluation framework for its product, as reported by Mind the Product. The framework was built from real lawyer time-entry records, scored outputs positively, and deducted for hallucinations and irrelevant content. The detail that matters for a smaller firm is not the scale but the principle: define the costliest errors first, then build tests that specifically target those errors.
Where will you actually encounter one?
Owner-managers often encounter the idea of evals when something goes wrong: a wrong answer in a customer response, a quote with a calculation error, a case summary that missed a key detail. The eval is the thing you build afterwards to make sure it doesn’t happen again. But the better move is to build a basic suite before you go live, and run it whenever something changes.
You will meet evals in three practical moments. The first is before deployment, when you are deciding whether the tool is good enough to use in a customer-facing workflow. The second is before any change to a prompt, a model version, or a policy document the AI references. If the updated version scores lower on your test set than the previous one, you have caught a regression before it reaches a client. The third is after a complaint or escalation: take the actual failed output, add it to your dataset, label what went wrong, and expand the rubric to catch that category of error in future.
Hustle Badger, a product team advisory, recommends integrating evals into the workflow so that changes are checked against the test set as the product evolves, rather than treating evals as a one-off exercise at launch. That discipline, more than any particular tooling choice, is what keeps a deployed AI system reliable over time.
When does an eval justify the effort?
A formal eval suite is worth building when the AI task is customer-facing, high-volume, or compliance-sensitive, and when errors carry real cost: a wrong quote, a misrepresented service term, a response that contradicts your policy. For internal drafting tools where a human always reviews the output before anything goes out, a structured checklist or occasional audit is a reasonable starting point rather than a full test suite.
Three signals that the task probably warrants a proper eval: the AI is producing content that reaches clients or third parties without a human review step; the task involves regulated information such as pricing, medical advice, or legal terms; or the volume is high enough that manual review of every output is not practical.
A counterpoint worth holding: if your team cannot agree on what a good output looks like, the rubric will be inconsistent, and an inconsistent rubric produces an unreliable eval. That disagreement is useful information. It means the task is probably not yet defined clearly enough to automate with confidence. The conversation about what good looks like, before you build the eval, is often the most valuable part of the process.
Related terms you’ll come across
The eval conversation comes with vocabulary borrowed from software engineering. A golden dataset is a curated set of inputs and their ideal outputs, used as your baseline for comparison. A rubric defines what each score level means, so different people rating the same output reach the same conclusion. Understanding these terms helps you have the right conversation with a vendor or a developer without getting lost in the jargon.
A deterministic check is a rule that can be validated automatically, with a yes or no answer: is the response under 200 words? Does it contain the required reference number? Does it avoid the phrase “we’ll be in touch shortly” that your firm has prohibited in client communications? These checks are fast to write and easy to run consistently.
An LLM judge is an AI system used to score other AI outputs automatically. It speeds up testing, but carries its own risks. Scoring can be inconsistent unless the rubric is well-defined and tested against human-labelled examples first. Human review of edge cases and high-risk outputs remains important even when an automated judge is in place.
An A/B test is different from an eval. An A/B test measures whether version B produces better business outcomes than version A, such as higher conversion or fewer escalations. An eval checks whether the outputs themselves are correct. A prompt might score well in an eval but still underperform in an A/B test, or vice versa. Both are useful, but they answer different questions and should not substitute for each other.
The term “AI eval” sounds more technical than it is. In practice, it is the answer to a simple question: how do we know this thing is working? For a services firm, the minimum viable version is a handful of real examples, a rubric that defines good and bad, and the habit of running it before anything changes. That habit, more than the sophistication of the tooling, is what separates a disciplined AI rollout from an optimistic one.
