What Are AI Evals?

Why do you need AI evals?

You need AI evals to ensure your automated systems work as expected every time they run. When you build an AI agent, it might work well on five test cases. It might fail on the sixth. Evals force the system to run through hundreds of examples to prove it is ready for real use.

How do AI evals work in practice?

Evals work by comparing the AI output against a known correct answer or a set of strict rules. You create a list of inputs and the desired results. The system runs these inputs through your AI. It then scores the output based on how well it matches your requirements. If the AI changes its mind or hallucinates, the test fails.

What happens during an AI eval process?

During an eval, your system processes a dataset of representative tasks. These tasks should mirror the actual requests your customers send to your business. If you are building a customer support bot, your eval dataset includes common questions about refunds, shipping, and product features. The system logs every success and failure for your review.

Can you automate the evaluation process?

You can automate the evaluation process by using a secondary AI model to grade the results of your primary system. This is often called model-graded evaluation. The secondary model acts as an impartial judge. It checks for tone, accuracy, and adherence to your business rules. It flags errors for your team to fix.

What is the difference between testing and evals?

Testing is a broad term for checking if your software works. Evals are specific to the non-deterministic nature of AI models. Traditional software tests check if a button clicks or a page loads. AI evals check if the model understood the intent and provided a truthful, helpful, or safe response.

How do evals prevent AI hallucinations?

Evals prevent hallucinations by establishing a baseline for factual accuracy. You include known facts in your test dataset. If the AI provides information that contradicts these facts, the eval catches it. You can then adjust your RAG system or prompt instructions to ensure the model stays within the bounds of your data.

When should you run AI evals?

You should run AI evals every time you change your prompt or update your knowledge base. Even small changes to instructions can cause unintended behavior in an AI agent. Running a full suite of evals confirms that your update improved the system rather than introducing new errors into your workflow.

Who should manage your AI evals?

Your technical team or an automation partner should manage your AI evals. They set up the testing framework and maintain the datasets. This ensures that your AI remains reliable as your business grows and your requirements change. It turns the AI from a black box into a predictable business tool.

How do evals improve customer experience?

Evals improve customer experience by ensuring consistent and accurate communication. Customers trust your brand when the AI gives correct answers to their specific questions. By catching errors early through testing, you avoid the risk of sending bad information to your clients. This builds trust and keeps your operations running smoothly.

We build custom AI agents and RAG systems with rigorous evaluation frameworks to ensure your production automation remains accurate and reliable.