Large Language Model
AI SystemsA large language model (LLM) is an AI system trained on massive amounts of text data to learn patterns in language, enabling it to generate, interpret, and manipulate human-readable text. LLMs use neural network architectures—most commonly transformers—to understand context, infer intent, and produce coherent responses. They form the core of modern answer engines, retrieval-augmented systems, and conversational interfaces, shaping how information is synthesized and surfaced across AI-driven environments.
Overview
Large language models operate by predicting the next most probable token in a sequence, but at scale this becomes a powerful tool for reasoning, summarization, translation, and explanation. Training on diverse datasets allows LLMs to generalize across topics, handle ambiguous queries, and generate structured or unstructured text with high fluency.
Why It Matters
LLMs determine how content is interpreted and represented in AI-generated answers. Their training data, structure, and retrieval mechanisms shape which sources they trust, which ideas they surface, and how they fuse information into a final output. Understanding LLM behavior is foundational to understanding AI Visibility, because these models increasingly act as the gatekeepers through which users encounter information.
Mentioned in Blog Posts
Why one-shot retrieval breaks on complex tasks and how agentic RAG with ReAct, tool registries, and self-evaluation loops upgrades the stack.