How to Conduct AI Evals: Best Practices for Building AI Confidence

AI is quickly becoming a must-have layer when it comes to the future of customer experience. Whether it’s next-gen chatbots, copilots or AI-driven experiences, companies are finding a variety of new ways to implement AI throughout the user journey. But today, the gap between AI ambition and AI confidence remains significant. Although the potential outcomes of these AI systems are promising, making sure that they work in the real world and operate with a focus on safety and reliability is paramount for success.

To close that gap and ensure that AI systems are running smoothly, proper evaluations are critical. This blog will share best practices for conducting AI evals using automation, AI and human perspectives, setting you up to reduce AI risk and release with confidence.

What are evals and how do they fit with QA testing?

Simply put, evals are quality checks for AI systems. AI is non-deterministic, meaning that unlike traditional software, you can’t just test a new feature or code that has changed to see if it passes a test case and call it a day. The same input might lead an AI system to produce a brilliant output on the first attempt and hallucinate on the second. That’s why evals are the gold standard for assessing AI performance: Rather than relying on subjective feedback, they provide objective data that gives you a precise measurement of how an AI system is performing.

There are several different attributes that evals can test, including:

• Performance & utility: Does the AI actually do the job?
• Brand & user experience: Does the AI sound like your brand voice?
• Safety & compliance: Does the AI meet regulatory requirements?

Assessing performance and utility boils down to determining if the AI is providing factually correct information and solving the specific problem a user presents. Evaluations of brand and user experience focus on the AI’s tone and voice, including how it adapts to different user interactions, as well as latency. And of course, safety and compliance evals are key for adhering to industry regulations and protecting sensitive information.

Why are AI evals becoming popular in 2026?

Everyone is building autonomous agents right now. In fact, Gartner expects autonomous agents to jump from under 5% of LLM usage in 2025 to 40% by the end of 2026. And as more agents are deployed into production, AI evals are increasingly crucial for unlocking the insights that determine where AI systems are succeeding or falling short. Evals provide information that helps organizations to not only fine-tune how their system works, but demonstrate compliance with emerging regulations, like the EU AI Act..

The 4 main steps of AI evals

1. Recruit the right experts

Recruiting the right people to grade an AI system is the first step. In all cases, these evaluations require the support and involvement of either domain experts (like lawyers, doctors and finance professionals) or generalists who may not be a subject matter expert but fit the right persona (like seniors, athletes and budget-conscious shoppers).

2. Build a rubric and golden dataset

The next step: develop the eval framework itself. This is a critical step and one where Applause provides guidance on what attributes to evaluate and the best scoring rubric to use. Since AI is non-deterministic, creating a golden dataset is crucial for understanding which outputs are accurate, relevant, and aligned with other key assessment criteria so that you can grade and rate responses appropriately.

3. Scale with tooling

Next comes the actual execution of the evaluation. This can be completed manually or supported by tooling such as a LLM-as-Judge (a framework that orchestrates multiple AI judges, detects disagreements, and escalates to human experts). In instances where AI models assist in the evaluation, human-in-the-loop comes into play to review outputs where a model reports a low level of confidence or multiple models return disparate results.

4. Deliver statistically rigorous analysis

The final step is the analysis phase, where results are analyzed and parlayed into meaningful feedback for development teams so that they can:

• Understand exactly how the AI system performed.
• Identify where its strengths and weaknesses are.
• Use that data to find out what problems exist.
• Fine-tune the system and feed back into the model for reinforcement.

This analysis gives businesses the actionable information they need to build AI confidence and close the gap between expectations and reality.

The grader: How is a score assigned?

The grader is the person or entity that comes up with the prompt set or uses a provided set of prompts to evaluate an AI system’s outputs across a number of attributes. This evaluation ultimately helps teams understand the quality of their system, including where it’s performing well and where it’s not. You cannot have an eval without a grader.

There are 3 types of graders:

• Code-based: Fast, cheap and deterministic unit tests.
• Model-based: AI-driven solutions are used to support the eval of the model’s output. These are open-ended evals that require calibration with human graders.
• Humans: Subject matter experts develop the right rubric and scenarios, as well as a golden dataset, to review the model’s output and deliberate on any model disagreements.

Running thousands of evals relying solely on humans as graders is too expensive for most organizations, so using models is crucial for scaling cost-effectively. That being said, even in model-based evals, human perspectives remain key for establishing rubrics and looking at disagreements in the models.

How do evals fit with traditional QA testing?

Traditional QA typically involves exploratory testing and manual test cases, where real humans test known, repeatable scenarios. But with the rise of AI, QA is changing in a number of different ways. Now, AI-native automation, AI evaluations and red team testing provide organizations with the ability to confidently evaluate their AI system’s performance despite its non-deterministic nature.

AI evaluations in particular are a new and important opportunity, as they allow companies to stress-test AI outputs at scale to ensure quality, safety and accuracy. This approach enables continuous monitoring and feedback that contribute to ongoing performance improvements in an AI system.

Top use cases for AI evals

If you have a customer experience agent that uses voice features, it’s very difficult to effectively perform a model-based eval. Factors like different accents, alphanumeric characters and latency issues make it challenging for an LLM to properly test the system. In this instance, humans are required for successful evals.

Shopping is another area where evals can have a meaningful impact. Although agentic shopping is growing in popularity throughout the marketplace, the full shopping journey is still challenging for agents to get right. For ecommerce retailers, these agents have to coordinate multiple systems on the backend and it’s easy for them to make mistakes, making AI evals very valuable.

In regulated industries, the bar for safety, compliance and accuracy is extremely high. This means the cost of an AI error isn’t just a bad user experience, but rather a legal, financial or safety catastrophe. Thus, AI evals are especially critical in the finance, healthcare, insurance and legal industries.

Then there’s the EU AI Act, which is perhaps the most stringent AI regulation globally. It makes human oversight a legal requirement for many systems. While portions of this act are already in effect, the deadline for high-risk AI compliance is on August 2, meaning now is the time for customers in the EU to get human perspectives on their AI systems.

How Applause can help with AI evals

Applause’s proprietary framework for evaluating AI at scale combines three components:

1. Our customizable AI judging engine.
2. Our global network of domain experts.
3. Our statistical analysis methodology.

Think of it as the full testing rig — not just the software, but the experts, the process and the evidence it produces. However, this is not an all-or-nothing solution. Sometimes clients might just want human graders, or advisory help on developing the rubric. Whatever the case may be, Applause is flexible and willing to help customers meet their unique needs. What sets Applause apart from other approaches:

• Managed independence: We aren’t just a tool; we are a holistic solution that combines tooling, expertise and an expert community, acting as a neutral third party that supports the comprehensive evaluation of AI systems.
• Statistical confidence: Our mature methodology provides the rigorous mathematical coverage and measurable confidence needed to prove to regulators that a system is safe and accurate.
• Targeted fine-tuning: We identify where a system is performing poorly so engineers have the data they need to fix specific hallucinations or logic gaps.

Overall, this eval serves to help customers pinpoint relative strengths and weaknesses of their AI systems so that they can use that information to improve them.