OTUS: why language models hallucinate and what measures reduce the risk of errors

Language Model Hallucinations — these are not a rare glitch, but a fundamental property of generative AI: the system can sound confident even where it lacks facts. For teams embedding LLMs into products, support, analytics, or internal processes, this means not just inaccuracy, but a concrete operational risk.

Where the Error Comes From

A language model does not verify truth the way a human or search engine does. Its task is to predict the most likely continuation of text based on a massive dataset and statistical relationships between words. If the request lacks context, the formulation is ambiguous, or training data contains few reliable examples, the model still strives to deliver a coherent response.

This gives rise to the effect users perceive as a lie: the text looks logical, the tone confident, yet the facts can be invented or conflated. The problem is amplified in scenarios where the model is expected to provide precise citations, numbers, legal formulations, medical recommendations, or code. In such tasks, an LLM can not only confuse the source but also fill in missing details by template: invent a study, cite a non-existent law, name an incorrect API version, or propose a seemingly functional but unsafe code snippet.

The more plausible the answer appears on the surface, the higher the risk that the error passes further down the process without verification.

Why Fine-Tuning Alone Is Not Enough

The intuitive idea that "let's just fine-tune the model and eliminate hallucinations" works only partially. Fine-tuning does help the model behave better in a specific domain, follow response format, and less often venture into obvious fabrication. But it does not transform the model into a system that knows only verified facts and can reliably stop when data is insufficient.

The model continues to be optimized for plausible text, not for the truthfulness of each statement. Even large and well-tuned models continue to err on rare cases, recent events, narrow specialized topics, and long chains of reasoning. The more steps between question and answer, the greater the chance that inaccuracy will appear in one of the links.

This is why the problem cannot be solved by a single temperature adjustment, one new dataset, or a magic prompt. What is needed is an architecture in which the model is not the sole source of truth and does not make critical decisions without external support.

How to Reduce Risk

In practice, the working approach is not to expect flawless behavior from an LLM, but to build protective layers around it. The more costly the error is for the business, the more checks, constraints, and explicit rules for declining to answer should be in the pipeline. This shifts the approach to deployment: instead of the question "how do we force the model never to be wrong," a different one emerges — "how do we ensure an error does not become an incident." And this is already a question of system design, not model magic.

• Connect retrieval and have the model answer only from found documents
• Require citations to specific data fragments, not abstract sources
• Separate generation and validation: one step writes the answer, another checks facts and format
• Limit the task scope so the model does not improvise beyond the domain
• Add human-in-the-loop for legal, financial, medical, and production scenarios

Separate importance lies in monitoring and testing. The team needs sets of test cases, metrics by error types, and a log of situations where the model declined to answer or delivered an incorrect result. It is useful to compare LLM behavior against ordinary deterministic rules and see where automation truly accelerates work and where it creates hidden risk. If the system writes code, communicates with customers, or draws conclusions from data, errors must be analyzed as systematically as bugs in an ordinary product.

What This Means

Hallucinations are not an annoying exception but a limitation of the technology class. This means that winning teams are not those who blindly trust the intelligent answer, but those who design the LLM as a probabilistic component with checks, boundaries of application, and a clear understanding of the error's cost.