This phenomenon is called hallucination and is one of the biggest challenges in the practical implementation of LLMs. In this article, we will take a closer look at what hallucinations are, why they occur, how they can be counteracted with the help of RAG (Retrieval-Augmented Generation) architecture, and how the x-talk platform supports companies in building more reliable AI solutions.
Hallucinations in LLMs – what happens when the model “makes things up”?
Language models (LLMs) are trained on huge datasets of text – from books, to articles, to websites. Thanks to this, they are able to generate coherent and grammatically correct statements. However, it is important to emphasize one thing: an LLM does not understand the world in the same way a human does. It has no knowledge, self-awareness, or ability for introspection. In other words, models do not know that they “do not know.”
Instead, they operate on the principle of statistical prediction: for a given sequence of words, they try to predict which next word is most probable. The problem arises when the model encounters a question to which it does not know the answer or lacks the right data. Instead of replying “I don’t know,” the model – by its very design – continues to generate a response. And often one that sounds very convincing.
An example? Ask the model: “Who invented yellow concrete and in which year?” If such a concept as “yellow concrete” does not exist in any known data, the model may still respond, inventing a name, a research institution, or even citing a non-existent source. It sounds realistic, but it is entirely fabricated.
Hallucinations are particularly dangerous in applications where specialized knowledge is required – e.g. in medicine, finance, law, or industry – where incorrect information can lead to costly decisions or even life-threatening situations.
RAG – how to enrich the model with knowledge
To increase the accuracy and relevance of answers generated by language models, the RAG (Retrieval-Augmented Generation) approach was developed. This architecture works in two steps:
- First, the engine searches for matching documents or text fragments from an external knowledge base.
- Then, the LLM generates a response based on this specific data, not just its own statistical memory.
Sounds like a golden solution? In many cases, yes, but when using this technique, you may also encounter challenges.
Challenges in working with RAG
Lack of relevant information in the database
Sometimes the RAG engine cannot find any documents corresponding to the query – for example, because the topic is new, niche, or simply absent from the searched base. What then? Ideally, the system should recognize this lack of knowledge and clearly communicate it, e.g.: “I do not have sufficient information to answer this question.” Unfortunately, many systems still try to “save face” with guesses – which brings us back to the problem of hallucinations.
Low contextual relevance
Another issue is when the search engine returns documents containing keywords but not substantively related to the query. For example, a query about “eco-friendly architecture” may return a text about “eco-friendly concrete” but not about building design. This leads to answers that seem relevant but are misleading. That is why it is crucial to apply confidence thresholds – if the relevance of results is too low, it is better to withdraw from answering than to provide uncertain data.
Signaling lack of knowledge
A few fabricated answers can irreversibly damage the trust of a user, who may then stop using the tool. That is why it is worth teaching the model to admit lack of information. One strategy that can be implemented in RAG systems is to assess the relevance of retrieved documents. If none of them reach a certain confidence threshold, the model should, instead of “guessing,” clearly communicate that it does not have enough knowledge to provide a reliable answer. An example of such a response: “Unfortunately, based on the available information, I am not able to provide a precise answer to this question.”
How to implement a RAG-based system?
Check out the solution ready for your knowledge base – X-TALK supports companies in building and deploying RAG-based systems that not only increase accuracy but also enable control over the quality of responses. Thanks to flexible architecture, integration with external databases, and expert support, it is possible to build an AI system that not only answers, but above all knows when it should not answer.
Hallucinations in LLMs are a real problem, but not an insurmountable one. RAG architecture is a step toward more reliable systems, and tools like x-talk help organizations effectively implement these solutions, tailored to their needs and real challenges. After all, in the world of AI, just as important as giving good answers is being able to say: “I don’t know” – and doing it wisely.
