There’s a moment that keeps happening in design studios across the world. A product manager runs a standard A/B test on their new AI feature. The results come back clean. Task completion rates look good. Time-on-task is acceptable. Users seem satisfied in the exit surveys.
Then the feature ships. And within weeks, support tickets start flooding in. “The AI gave me completely different answers to the same question.” “It sounded confident but was totally wrong.” “Yesterday it worked perfectly, today it’s useless.”
The PM stares at the test results, confused. Everything measured correctly. So what went wrong?
The traditional UX evaluation methods for measuring AI products are no longer working.
The Binary Success Problem
Traditional UX evaluation was built for a deterministic world. You design a button. You test whether users can click it. You measure how long it takes. You count errors. The math is clean because the system is predictable.
A model can score high on accuracy in lab tests yet still deliver answers that erode user trust in the real world. This is the fundamental break. AI-powered products aren’t just unpredictable, they’re probabilistic by design.
In June 2025, a Deloitte survey found that one-third of generative AI users had already encountered incorrect or misleading answers. These weren’t edge cases or rare failures. They were normal operating conditions.
A/B test or task completion metrics or System Usability Scale (SUS) wasn’t designed for systems that behave differently every time
The same prompt yields different outputs. Success isn’t binary anymore. An AI might give you an answer that’s technically accurate but tonally disastrous. Or one that sounds authoritative but is fabricating facts. Or one that’s perfect for one user and completely misses the mark for another asking the exact same question.
Your A/B test can’t catch this. Your task completion metrics can’t measure it. Your System Usability Scale (SUS) wasn’t designed for systems that behave differently every time.
UX Evaluation Methods for AI Products
For UX and product design professionals, AI evaluations aren’t about any one evaluation metric like latency or hallucination rate. They’re about understanding how an AI agent or AI product strategy performs under real-world conditions, how it affects product experience, and whether it delivers value aligned with product goals.
The challenge is that “real-world conditions” now includes dimensions traditional UX evaluation methods never had to consider:
Hallucination rates. 77% of enterprises fear AI hallucinations according to Deloitte’s latest survey. With GPT-4 still lying 15% of the time and legal AI tools fabricating cases in 82% of queries, one wrong AI response could cost your company millions in lawsuits, compliance violations, or customer trust.
Consistency across outputs. Does your AI give similar quality responses to similar prompts, or does it vary wildly?
Tone and brand alignment. The AI might be factually correct but sound nothing like your brand voice.
Contextual relevance. Top-tier implementations achieve 90-95% relevance for retrieved content. Anything below 80% typically signals retrieval pipeline issues, not LLM flaws.
Trust signals. Can users tell when the AI is confident versus guessing?
None of these fit neatly into your existing evaluation framework. And that’s the problem.
The New Evaluation Stack
Traditional ML metrics like accuracy or recall tell you whether a model works. But they don’t tell you whether it’s safe, fair, or consistent enough to ship. Smart teams are building evaluation stacks that layer multiple approaches:
Automated Metrics for Breadth
These catch obvious failures across thousands of examples. Tools have emerged specifically for this:
- Promptfoo – An open-source framework for running targeted evaluations at scale. Teams can define their own test datasets, automate comparisons between models, and log metric evolution through each iteration
- DeepEval – Functions like Pytest but for LLM outputs, measuring everything from answer relevancy to hallucination rates
- Langfuse – Tailored for LangChain or LangGraph-based applications, it delivers detailed trace logs, step-by-step replay, and output scoring, ideal for diagnosing logic errors in agent workflows
Hallucination Detection Tools
This category didn’t exist three years ago. Now it’s critical infrastructure:
- Patronus AI – Specializes in hallucination and safety detection. It identifies factual errors, bias, and policy violations in real time, supporting compliance-critical use cases in finance or healthcare
- Galileo AI – Offers model review without requiring labeled reference data. Its chain-based scoring and drift detection help identify quality degradation before it impacts users
- W&B Weave and Arize Phoenix – Benchmarked across 100 test cases, both delivered nearly identical accuracy at 91% and 90% respectively in hallucination detection, with W&B Weave leading at 86% recall
Expert Panels for Depth
Automated tools catch quantity. Humans catch quality. According to Stanford research on evaluation frameworks, combining automated and human evaluation improves agent quality metrics by 40%.
This means having designers or domain specialists review representative samples. Not every output, that’s impossible at scale. But enough to catch the subjective failures that metrics miss.
User Testing for Reality Checks
Because ultimately, what matters is whether real people find the experience valuable. But user testing AI products requires different protocols:
- Test the same prompt multiple times to catch variance
- Ask users to rate confidence, not just correctness
- Track what happens when the AI gets it wrong
- Measure recovery patterns when users refine their prompts
The Custom Metrics Imperative
Here’s what separates teams shipping successful AI products from those stuck in pilot purgatory: custom metrics.
Begin by specifying exactly what you want the AI to do. The key is clarity: a well-defined task ensures your evaluation focuses on outcomes that matter. Next, determine the criteria that define a successful output. For LLMs, correctness is rarely binary.
A customer support bot needs different metrics than a code generation tool. A creative writing assistant needs different metrics than a financial analysis system.
Generic metrics tell you if something is technically working. Custom metrics tell you if it’s working for your specific use case.
A customer support bot needs different metrics than a code generation tool. A creative writing assistant needs different metrics than a financial analysis system. What’s the purpose of this AI in your product, and what outcomes are you aiming to deliver? This is where many AI product managers, data product managers, and even product analysts stumble. They default to measuring what’s easy instead of focusing on what actually matters to users and the business
Real-World Implementation
In the Kravet Internal AI project, initial accuracy during the pilot phase was below 60%, despite the assistant being trained on large volumes of internal documentation. The primary causes were not model limitations, but data quality and retrieval issues: conflicting and outdated files, unreadable formats, inconsistent product specifications, and unpredictable source selection within the RAG pipeline.
The fix wasn’t better models. It was better evaluation:
- Defined correctness criteria jointly with the client
- Labeled responses as correct, partially correct, or incorrect
- Tested against a fixed set of real employee questions after each iteration
- Improved retrieval by prioritizing structured systems and removing low-quality content
One common misconception is treating these systems as one-time projects: plan, build, and roll out. In reality, agents behave much more like digital products: you design, deliver, launch, and continuously improve. And this improvement should be grounded in data. We’ve seen agents launch with a 20% containment rate, and after a focused modification sprint, reach 60% or more.
What This Means for UX Professionals
You can’t rely on task completion rates and A/B testing alone anymore. Success isn’t binary with generative systems.
You need to learn new tools. Not just how to use them, but when to use them and what they’re actually measuring. Promptfoo for systematic testing. DeepEval for unit testing outputs. Langfuse for tracing. Patronus for hallucination detection.
In 2026, trust can only be measured by evaluation methods that account for the probabilistic, non-deterministic, sometimes-brilliant-sometimes-disastrous nature of AI systems.
Most importantly, you need to design custom metrics specific to your use case. The generic ones tell you the model works. The custom ones tell you whether it works for your users.
In 2026, accuracy is table stakes. Trust is the differentiator. And trust can only be measured by evaluation methods that account for the probabilistic, non-deterministic, sometimes-brilliant-sometimes-disastrous nature of AI systems.
The evaluation crisis is real. But the tools to solve it exist. The question is whether UX professionals will adapt their workflows fast enough to use them.
