What an LLM actually is
A large language model is a system trained to predict the next piece of text, which lets it generate fluent, useful language. It is a powerful pattern-matcher, not a knower of facts, which is why grounding and review matter.
Prediction, not knowledge
An LLM predicts likely text based on patterns in its training data. That makes it fluent and versatile, but it does not “know” facts, which is the root of both its usefulness and its tendency to hallucinate.
Understanding that an LLM predicts text rather than retrieves facts explains almost everything about how to use it. Its fluency comes from patterns learned across vast text; its errors come from the same place, it will confidently predict plausible text even when the facts are wrong. This is why the reliable business pattern is to pair the model’s language ability with grounding in your real data (RAG) and human review, rather than trusting it as an oracle. The model supplies fluency; your data and people supply truth.
Frequently asked questions.
Is a bigger LLM always better?
No. Bigger models cost more per token and are not always more accurate for a specific task. For most business uses, a smaller model grounded in your data with RAG outperforms a larger ungrounded one, and costs far less to run at inference, which is where most spend accumulates.