Systematically uncover KI hallucinations: Why traditional testing methods fail

Language models are masters of persuasion—even when they lie. An AI agent can claim to have created database entries that never existed or insist on performing actions it never initiated. For production teams, distinguishing between genuine errors and fabricated results is crucial. It not only affects troubleshooting but also influences user trust in the system.

The key challenge: How to reliably detect when a model is not just failing but actively constructing information? Dmytro Kyiashko, a software developer specializing in AI system testing, has pondered this question for years. His insights reveal that the problem is deeper than initially assumed.

The fundamental difference: Error vs. Hallucination

Conventional software errors follow predictable patterns. A broken function returns an error. A misconfigured API provides an HTTP status code and a meaningful error message. The system signals that something went wrong.

Language models fail differently—and more insidiously. They never admit to ignorance. Instead, they produce plausible-sounding answers for tasks they haven’t performed. They describe database queries that never occurred. They confirm the execution of operations that only exist in their training data.

“Every AI agent operates according to instructions prepared by engineers," explains Kyiashko. “We know exactly what capabilities our agent has and what it doesn’t." This knowledge forms the basis for a fundamental distinction: If an agent trained for database queries silently fails, that’s a mistake. But if it returns detailed query results without touching the database, that’s a hallucination—the model has invented plausible outputs based on statistical patterns.

Proven strategies for validation

The core principle: verification against the system’s fundamental truth. Kyiashko employs multiple tests to uncover AI hallucinations.

Negative tests with access control: An agent without database write permissions is specifically prompted to create new records. The test then checks two things: First, whether unauthorized data appeared in the system. Second, whether the agent falsely confirmed success.

Real-world data as test cases: The most effective method uses actual customer conversations. “I convert the chat history into JSON format and run my tests with it," reports Kyiashko. Each interaction becomes a test case, analyzing whether the agent made claims contradicting system logs. This approach captures edge cases that synthetic tests might miss—because real users create conditions that developers could never anticipate.

Two complementary evaluation levels:

Code-based evaluators perform objective checks. They validate parsing structures, JSON validity, SQL syntax—all verifiable in binary.

LLM-as-Judge evaluators are used when nuances matter: Was the tone appropriate? Is the summary accurate? Is the response helpful? For this approach, Kyiashko uses LangGraph. Effective testing frameworks employ both methods in parallel, as neither approach alone suffices.

Why traditional QA skills don’t transfer

Experienced quality engineers face limitations when testing AI systems. Assumptions that work in traditional software quality assurance can’t be directly transferred.

“With traditional QA, we know the exact output format, the precise structure of input and output data," says Kyiashko. “When testing AI systems, that’s not the case." The input is a prompt—and the variations in how users formulate their requests are practically unlimited.

This requires a fundamental paradigm shift: continuous error analysis. It means regularly monitoring how agents respond to real user queries, identifying where they invent information, and continuously updating test suites.

The challenge is amplified by the volume of instructions. Modern AI systems require extensive prompts defining behavior, boundaries, and context rules. Each instruction can interact unexpectedly with others. “One of the biggest problems is the enormous number of instructions that must be constantly updated and retested," observes Kyiashko.

The knowledge gap is significant. Most engineers lack a structured understanding of appropriate metrics, effective dataset preparation, or reliable methods for validating varying outputs.

The hidden truth: Testing is more expensive than development

Here lies an uncomfortable truth: “Developing an AI agent isn’t difficult," notes Kyiashko. “Automating the testing of that agent is the real challenge."

In his experience, more time is spent testing and optimizing AI systems than creating them. This reality calls for a rethink in staffing and resource allocation.

From concept to practice: Reliable release cycles

Hallucinations erode trust faster than traditional errors. A functional bug frustrates users. An agent confidently delivering false information destroys credibility permanently.

With Kyiashko’s testing methodology, reliable weekly releases become possible. Automated validation detects regressions before deployment. Systems trained on real data handle most customer inquiries correctly. Weekly iterations enable rapid improvements: new features, refined responses, expanded domains—all controlled and validated.

The industrial necessity

The world has long recognized the potential of generative AI. There’s no turning back. Startups emerge daily with AI at their core. Established companies integrate intelligence into their core products.

“Today, we need to understand how language models work, how AI agents are built, how they are tested, and how to automate validations," argues Kyiashko. Prompt engineering is becoming a fundamental skill for quality engineers. Data testing and dynamic data validation follow. These should already be part of the standard skill set for testing engineers.

The patterns Kyiashko observes in the industry—through technical paper reviews, startup evaluations, and technical forums—paint a clear picture: teams worldwide face the same problems. Validation challenges that only pioneers in production environments solved years ago are now universal concerns as AI deployment scales.

A diversified testing framework

Kyiashko’s methodology covers evaluation principles, multi-turn conversations, and metrics for different error types. The core concept: diversification.

Code-level validation detects structural errors. LLM-as-Judge evaluation assesses effectiveness and accuracy depending on the model version. Manual error analysis identifies patterns that automated tests overlook. RAG tests verify whether agents utilize provided context or invent details.

“Our framework is based on a versatile approach to testing AI systems—combining code-level coverage, LLM-as-Judge evaluators, manual error analysis, and retrieval-augmented generation assessment," explains Kyiashko. Multiple validation methods working together capture various hallucination types that individual approaches might miss.

What’s next

The field defines best practices in real time. More companies deploy generative AI. More models make autonomous decisions. As systems become more powerful, their hallucinations become more plausible.

This is not a reason for pessimism. Systematic testing detects inventions before they reach users. It’s not about perfection—models will always have edge cases. It’s about systematically capturing and preventing inventions from reaching production.

These techniques work when applied correctly. What’s missing is a widespread understanding of how to implement them in production environments where reliability is critical.

Dmytro Kyiashko is a Software Developer in Test specializing in AI system testing, with experience building test frameworks for conversational AI and autonomous agents, as well as expertise in reliability and validation challenges of multimodal AI systems.