Large Language Model

A large language model (LLM) is a type of AI model trained on massive amounts of text data to predict the most likely next token in a sequence. This simple mechanism enables LLMs to generate fluent text, translate languages, write code, answer questions, and perform a wide variety of reasoning-like tasks.

Key characteristics

• Generative capability – Produces coherent text, code, or structured outputs from prompts
• Instruction following – Can understand and execute complex, multi-part instructions
• Tool-use capability – Can decide which tools to call and format requests (though cannot execute them)
• Statistical foundation – Learns patterns from billions of words in training data
• Versatility – Can be applied to domains from writing to programming to planning
• Context sensitivity – Uses a prompt + context window to shape its outputs
• Few/zero-shot adaptability – Performs new tasks with little or no task-specific training
• Emergent behavior – Exhibits surprising generalization (e.g., chain-of-thought reasoning)

LLMs are the foundational intelligence layer of modern AI systems, but by themselves they lack memory, persistence, tool access, or governance.

Understanding the relationships

LLM vs. AI agent vs. AI system

• LLM = Foundation: The raw intelligence/reasoning engine
• AI agent = LLM + Agency: Adds goal-seeking, decision-making, and tool orchestration
• AI system = Complete Stack: Adds infrastructure, memory, persistence, governance

AI System    ↑AI Agent    ↑  LLM

What LLMs can do alone

• Generate text, code, and structured outputs from prompts
• Reason through problems and follow multi-step instructions
• Decide which tools to use and format tool calls (function calling)
• Transform and analyze content within their context window
• Answer questions based on training data and provided context

What LLMs cannot do alone

• Execute tool or API calls (need orchestration infrastructure)
• Maintain long-term memory or state across sessions
• Access real-time data or external systems directly
• Implement retries, complex workflows, or observability
• Enforce runtime policies or permissions
• Persist information between conversations

Key distinction: An LLM is not an AI agent or an AI system, it is the core model that those higher layers build on.

Examples

• Text generation – Drafting essays, stories, or articles from prompts
• Code generation – Writing snippets, functions, or even full programs
• Tool selection – Analyzing user intent and choosing appropriate tools/functions
• Language translation – Converting text between natural languages
• Summarization – Condensing long articles or documents into key points
• Question answering – Responding to factual queries based on training data
• Conversation – Engaging in natural-sounding dialogue or roleplay
• Classification – Categorizing text (e.g., sentiment analysis, topic labeling)

Key point: An LLM is a predictive text model—powerful for generation and reasoning-like tasks, but not inherently able to use tools, maintain state, or coordinate workflows.

Non-examples

• AI agents – Agents use LLMs for reasoning but add decision-making, tool calls, and adaptation.
• Agentic systems – Full systems with infrastructure (clients, servers, orchestration) built around one or more agents.
• MCP components – Clients, servers, and hosts that provide access and runtime, not generative capability.
• Tools – External capabilities (e.g., APIs, databases) invoked by agents; not predictive models.
• Orchestration layer – Workflow management and reliability mechanisms; does not generate text or predictions.

Key point: An LLM is the raw generative core of modern AI, not the reasoning, action, or coordination layer.

The LLM is the language engine, but it needs interfaces, memory, tools, and orchestration logic to become something users can actually interact with productively.