Tool Use (LLM)
LLM tool use (also called function calling) is the ability of a language model to invoke external functions, APIs, databases, or systems based on the user's request — turning the LLM from a text generator into a controller that can act on the world.
Full definition
The model is given a JSON schema describing available tools; at inference time it can produce a structured tool-call rather than (or in addition to) free text. The host application executes the tool and feeds results back into the model's context. Tool use is the foundation of RAG, agents, and most production LLM applications. Major implementations include OpenAI Function Calling, Anthropic Tool Use, and the open-source equivalents in vLLM and llama.cpp.
Why it matters
Tool use is what makes LLMs useful for real workflows. Without tools, a model can only describe what to do; with tools, it can do it — query the database, send the email, file the ticket, run the code. Tool design (which tools to expose, how to structure them) is now a primary skill for AI engineers.
Example
A travel-planning assistant exposes 5 tools: search_flights, search_hotels, get_weather, get_user_preferences, and book. The LLM orchestrates them in response to "plan me a 4-day trip to Tokyo for under $3,000."
Related terms
- AI Agent / Agentic AIAn AI agent is an LLM-driven system that, given a goal, plans a sequence of steps, calls tools or APIs, observes the results, and iterates until the goal is reached — going beyond single-turn chat to multi-step autonomous action.
- Large Language Model (LLM)A Large Language Model (LLM) is a deep neural network — almost always a transformer — trained on hundreds of billions to trillions of words to predict the next token, and to generate, summarize, translate, or reason over text.
- Retrieval-Augmented Generation (RAG)Retrieval-Augmented Generation (RAG) is a technique where a language model retrieves relevant documents from an external knowledge base before generating a response, reducing hallucination by 30-60% and enabling citation of sources.
- Context WindowA context window is the maximum amount of text — measured in tokens — a language model can read and reason over in one inference call, equivalent to the model's working memory for that turn.
Source & further reading
Primary source: OpenAI — "Function calling and other API updates" (2023).
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