AI Evals

AI Glossary

AI Evals are simply tests that measure whether an AI’s outputs are actually good enough for your specific use case — think of them as a report card for your AI tool.

What it really means

When I talk about AI Evals with clients, I’m really talking about a process: you give an AI model a set of tasks, look at what it produces, and decide if that output is acceptable for your business. It’s not about whether the AI is “smart” in some abstract sense. It’s about whether it can reliably do what you need it to do — answer a customer’s question correctly, summarize a legal document without hallucinating facts, or write a follow-up email that doesn’t sound like a robot.

In practice, an eval is a structured test. You define what “good” looks like for your situation, then run the AI through a batch of examples. You score each output against your criteria. The result tells you if the model is ready for real use, or if you need to tweak your prompt, switch models, or add guardrails.

I’ve seen business owners assume that because a chatbot sounds confident, it must be accurate. That’s exactly why evals matter. They turn gut feelings into hard numbers.

Where it shows up

AI Evals show up in two main places: during development and after deployment.

During development: When I’m helping a client set up a custom AI tool — say, a customer support bot for a Maitland HVAC company — I’ll create a set of 50 to 100 test questions that cover common scenarios. “My AC stopped working at 3 PM on a Saturday — what do I do?” “How much does a new compressor cost?” I then grade the bot’s answers for accuracy, tone, and completeness. If it fails too many, I don’t let it near real customers.

After deployment: Once the tool is live, evals become ongoing checks. I might sample 5% of actual conversations each week and score them. This catches drift — when the model starts behaving differently because of updates or changes in how people phrase questions.

You’ll also see evals in vendor pitches. When a salesperson says “our AI achieves 95% accuracy,” ask what eval they used. The answer matters more than the number.

Common SMB use cases

Here’s where I see evals making a real difference for Central Florida businesses:

• Customer service chatbots: A Winter Park dental practice wants their bot to handle appointment booking and common questions about insurance. I create an eval set with 30 realistic patient queries, then check if the bot books correctly and doesn’t make up coverage details. One failed booking per hundred is probably acceptable. One hallucinated insurance policy is not.
• Document summarization: A downtown Orlando law firm uses AI to summarize deposition transcripts. The eval here is strict: I compare the AI’s summary against a human-written one for key facts, dates, and names. Missing a single date could mean a missed deadline.
• Content generation: A Lake Nona restaurant wants AI to write weekly social media posts. The eval checks for brand voice, factual accuracy (menu items, hours), and whether the post actually makes you hungry. I’ll have the owner score 20 sample posts before approving the tool.
• Internal knowledge base search: A Clermont pool service company uses AI to help technicians find repair procedures. The eval measures whether the AI returns the correct manual page for a given problem, and whether it does so in under five seconds.

Pitfalls (what gets oversold)

The biggest trap I see is people treating a single eval score as gospel. A model might score 98% on a generic benchmark but fail on the specific edge cases that matter to your business. For example, an AI that’s great at answering general HVAC questions might completely botch a question about a specific older model of heat pump that your Maitland clients still use.

Another common mistake: building an eval set that’s too easy. If you only test the AI on questions you already know the answers to, you’re not stress-testing it for the unexpected. I always include at least 10% “curveball” questions — things like misspelled words, angry customers, or requests that mix multiple topics.

Finally, some vendors will sell you “automated evals” that use another AI to grade the first AI. This can work, but it introduces its own errors. I’ve seen an AI grader give full marks to an answer that was factually wrong but sounded confident. Always have a human review a sample of the eval results, especially in the beginning.

The phrase “we have evals” can sound reassuring, but the real question is: do you have evals that actually test what your business cares about?

Related terms

• Prompt engineering: The art of writing instructions for an AI. Evals tell you if your prompt engineering is working.
• Ground truth: The correct answer that you compare the AI’s output against during an eval. Without ground truth, you’re just guessing.
• Hallucination: When an AI makes up plausible-sounding but false information. Evals are your main tool for catching hallucinations before they reach customers.
• RAG (Retrieval-Augmented Generation): A technique that gives the AI access to your own documents. Evals are essential here to check whether the AI is actually using the right documents or ignoring them.
• Fine-tuning: Training a pre-existing model on your own data. Evals measure whether the fine-tuning actually improved performance on your specific tasks.