Q: What is an LLM?

An LLM (Large Language Model) is an AI model trained to predict the next token in a sequence of text. That sounds too simple to be useful, but at massive scale it produces something remarkable: a system that can answer questions, write code, summarize documents, and reason through problems.

The "large" part matters. These models are trained on enormous amounts of text and have billions of internal parameters. During training, the model isn't taught facts directly. It learns patterns: grammar, logic, code structure, how concepts relate to each other. Everything it can do emerges from learning to predict text extremely well.

GPT (OpenAI), Claude (Anthropic), and Gemini (Google) are the frontier LLMs. They run on those companies' servers, and as an engineer you access them through API calls: you send text in, you get text back.

Two limits to understand from day one. An LLM only knows what was in its training data, so it knows nothing about your company or anything recent (that's what RAG is for). And it doesn't retrieve answers from a database, it generates them, which means it can generate wrong ones (this is called a hallucination). That's why evals and guardrails exist. Most of AI engineering is building around these two limits.