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:
Prompt Injection
Prompt injection is an attack where a user or data source inserts instructions that override a language model’s intended behavior.
RAG (Retrieval-Augmented Generation)
Retrieval-augmented generation (RAG) links a large language model to external documents, databases, and APIs so it retrieves relevant, up-to-date context at...
Accretive Software
Accretive software refers to AI platforms that automatically absorb model improvements as margin expansion by treating models as interchangeable components...