LLM (Large Language Model)
A large language model is a neural network trained on massive text corpora—often trillions of tokens—to predict the next word in a sequence. That simple objective, scaled up, produces systems that can write code, summarize legal briefs, translate languages, and hold surprisingly coherent conversations. GPT-4, Claude, Gemini, and Llama are all LLMs. The "large" refers to parameter count, typically in the billions, which correlates loosely with capability but tightly with compute cost. LLMs are general-purpose by default and useless for specific tasks until you shape their behavior through prompting, fine-tuning, or connecting them to your data. The model is the engine. Everything else—retrieval, guardrails, UI, evaluation—is what makes it drive straight.
Related terms:
Pace Layers
Pace layers separate complex systems into fast-changing parts that learn and slow-moving parts that stabilize, helping teams choose experiments vs. strategy.
Fine-Tuning
Fine-tuning continues training a pretrained language model on a smaller, task-specific dataset so it internalizes particular behaviors, styles, or domain...
AI Agent
An AI agent is a system that autonomously breaks a goal into steps—calling tools, reading results, and adjusting course—without waiting for a human prompt.