LLM (Large Language Model)

A large language model (LLM) is a transformer-based neural network trained on very large text collections to predict the next token in a sequence. Repeating that prediction lets it generate paragraphs, code, summaries, and answers.

Why it matters

LLMs power chatbots, coding assistants, search summaries, and document tools. They are accessed mostly through APIs billed per token, so latency, limits, and cost become operational concerns, as with any third-party dependency. Treat model output as a draft to verify, not a final answer.

Example

Given the prompt "The capital of France is", the model assigns probabilities to possible next tokens, picks one (" Paris"), appends it, and repeats until it decides to stop.

Common misconceptions

• LLMs look facts up in a database. They generate text from learned patterns, which is why hallucination occurs.
• Bigger always means better. Data quality, tuning, and task fit matter too.
• Their output is deterministic. Sampling settings such as temperature introduce variation.

Related

• Generative AI
• Context window
• Fine-tuning
• Rate limiting

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