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Why do LLMs hallucinate? Let's understand in simple words. An LLM hallucination is when the model gives us an answer that sounds confident but is completely wrong or made up. Now, the question is, why does this happen? The answer is simple. An LLM is a next-token predictor. It does not "know" facts. It predicts the most likely next word based on patterns it has seen during training. Means, the model is trained to be fluent, not to be factual. Let's say, we ask the LLM about a person it has never read about. The model still wants to give us an answer. So, it picks the words that sound most natural in that context. The output reads beautifully. But the facts can be completely invented. Here is the catch. The model does not have an "I do not know" button by default. It will try to complete the sentence, because that is what it was trained to do. A few reasons hallucinations happen: - The model predicts tokens, not truth. - It has no real-world grounding without external tools. - The training data has gaps, errors, and outdated information. - Fluency is rewarded during training, not honesty. So, how can we solve this problem? Here comes RAG, tool use, and grounding into the picture. We give the model real, verified context at the time of the question. Now, the model does not have to guess. It can read the actual source and answer. This is how we reduce hallucinations. The model is doing exactly what it was trained to do. It is our job to give it the right context.

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