What Is a Large Language Model?
A foundational explainer on how LLMs are trained and why they generate text the way they do.
What it actually is
A large language model is a neural network trained on enormous volumes of text to predict the next token in a sequence. That single objective — guess what comes next — turns out to be powerful enough to encode grammar, facts, reasoning patterns, and style, simply because predicting text well requires understanding it.
How training works
Training happens in stages: pretraining on a broad web-scale corpus builds general language competence, then instruction-tuning and reinforcement learning from human feedback (RLHF) shape the model to follow instructions, stay on-topic, and avoid unhelpful or unsafe responses.
Why it matters for builders
Understanding that an LLM is a statistical next-token predictor — not a database or a reasoning engine in the human sense — explains both its strengths (fluent, flexible language generation) and its failure modes (confident-sounding errors, or 'hallucinations'), which is essential context before wiring one into a product or an autonomous agent.
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