Making AI systems reliable: How to systematically detect and eliminate hallucinations

Generative AI models pose a fundamental challenge to development teams: they provide answers with absolute certainty, even when these are completely fabricated. An AI agent could claim to have generated database entries that never existed or report in detail about actions performed that it never initiated. Distinguishing between genuine system failures and AI-generated hallucinations is crucial for production.

From traditional software testing to AI validation

Conventional software development recognizes clear error signals: a faulty function returns an error code, a misconfigured API sends a distinct HTTP status code. The problem is predictable and reproducible.

AI systems fundamentally operate differently. They report successful completion of tasks they did not initiate. They cite database queries they never performed. They describe in detail processes that exist only in their training data — but the response appears completely plausible. The content is entirely invented.

This requires a completely new testing strategy. In classic QA testing, engineers know the exact response format, input, and output structure. With AI systems, this predictability does not exist. The input is a prompt — and the ways users formulate their requests are practically infinite.

The core strategy: validation against reality

The most effective method for hallucination detection is direct: verification against the actual system state. If an agent claims to have created records, it is checked whether these entries actually exist in the database. The agent’s claim is irrelevant if reality contradicts it.

A practical example: an AI agent without write access is asked to create new records. The test framework then validates that:

• No new data has appeared in the database
• The agent has not falsely reported “success”
• The system state remains unchanged

This approach works through various levels:

Unit and integration tests with defined boundaries: Tests intentionally perform operations for which the agent has no permission, validating that the system correctly rejects them.

Real production data as test cases: The most effective method uses historical customer conversations. These are converted into standardized formats — typically JSON — and run against the test suite. Each real conversation becomes a test case that reveals where agents make claims contradicting system logs. This captures edge cases and scenarios that synthetic tests overlook — because real users generate unpredictable conditions.

Continuous error analysis: Regularly reviewing how agents respond to actual user requests, identifying fabricated information, and continuously updating test suites. This is not a one-time process but ongoing monitoring.

Two complementary evaluation approaches

Experience shows that a single testing approach is insufficient. Two different strategies must work together:

Code-based evaluators for objective verification: They work best when the error definition is objective and rule-based. Examples include validation of parsing structures, JSON validity, or SQL syntax. These tests yield binary, definitive results.

LLM-as-Judge evaluators for interpretive assessments: Some quality aspects cannot be classified binarily. Was the tone appropriate? Is the summary correct and complete? Was the response helpful and factual? For these questions, a different model than a validator is needed — for example, using LangGraph frameworks.

Additionally, validation of Retrieval-Augmented Generation (RAG) becomes critical: tests explicitly verify whether agents actually use the provided context or instead invent details and hallucinate.

This combination captures different hallucination types that individual methods might miss.

Why traditional QA training is not enough here

Experienced quality engineers face difficulties when testing AI systems for the first time. The assumptions and techniques they have perfected over years cannot be directly transferred.

The core problem: AI systems have thousands of instructions — (Prompts) — that must be constantly updated and tested. Each instruction can interact unpredictably with others. A small change in a prompt can alter the entire system behavior.

Most engineers lack a clear understanding of:

• Suitable metrics for measuring AI system quality
• Effective preparation and structuring of test datasets
• Reliable methods for validating outputs that vary with each run

Surprisingly, the timing distribution is notable: creating an AI agent is relatively straightforward. Automating the testing of this agent is the real challenge. In practice, much more time is spent testing and optimizing AI systems than developing them initially.

Practical testing framework for scaling

The effective framework rests on four pillars:

1. Code-level coverage: Structural validation through automated, rule-based tests
2. LLM-as-Judge evaluators: Assess effectiveness, accuracy, and usability
3. Manual error analysis: Identify recurring patterns and critical errors
4. RAG-specific tests: Verify whether the context is used and not invented

These different validation methods together capture hallucinations that individual approaches might miss.

A practical example: when AI systems handle tasks like processing images — for instance, automatic recognition or content processing such as watermark removal — validation becomes even more critical. The system must not only report that it removed a watermark but also verify the actual change in the image.

From weekly to reliable releases

Hallucinations erode user trust faster than traditional software bugs. An error frustrates. An agent confidently providing false information destroys credibility and trust sustainably.

With systematic testing, a much faster release cadence becomes possible: reliable weekly deployments instead of months-long delays due to stability issues. Automated validation detects regressions before code reaches production. Systems trained and tested with real user conversations process the vast majority of actual requests correctly.

This rapid iteration becomes a competitive advantage: AI systems improve through adding new features, refining response quality, and gradually expanding their use cases.

Industry trend: AI testing as a core competency

AI adoption accelerates across all industries. More startups are founded with AI as a core product. More established companies integrate intelligence into their critical systems. More models make autonomous decisions in production environments.

This fundamentally changes the requirements for quality engineers: they must not only understand how to test traditional software. They now also need to understand:

• How large language models work
• How AI agents and autonomous systems are architected
• How to reliably test these systems
• How to automate validations

Prompt engineering becomes a core skill. Data testing and dynamic data validation are no longer niche topics — they are standard skills every test engineer should have.

Industry reality confirms this shift. Identical validation challenges are emerging everywhere. Problems that years ago were solved individually in production environments are now universal requirements. Teams worldwide face the same issues.

What systematic testing achieves — and does not

The goal is not perfection. Models will always have edge cases where they invent. The goal is systematic: identify hallucinations and prevent them from reaching users.

Techniques work when applied correctly. What is currently missing is a broad, practical understanding of how to implement these frameworks in real production environments where reliability is business-critical.

The AI industry currently defines its best practices through production errors and iterative refinement. Every hallucination discovered leads to better tests. Every new approach is validated in practice. This is how technical standards emerge — not through theory, but through operational reality.