AI Reference 058 Large Language Models Wikipedia guided Primary sources linked

Function Calling and Tool Use

Definition

Function calling and tool use let a model request structured actions such as querying a database, calling an API or using search, with the surrounding application executing the action.

What is function calling?

Function calling is a structured interface that lets a model request that surrounding software perform a defined operation. The model chooses a tool and produces arguments; the application validates and executes the call.

Examples of tools

A tool may search a database, retrieve weather data, create a calendar event, run code or update a business system. The model does not directly gain unlimited access to the computer; the application decides which functions exist and what permissions they have.

Why tool use matters

LLMs are strongest at interpreting and generating unstructured information. Tools let them connect that language ability to exact software operations and current data. Safe tool systems validate arguments, restrict permissions and handle errors outside the model.

Related terms, defined

Large Language Models AI Agents An AI agent is a system in which a model can select or plan actions over multiple steps, usually with tools, memory and feedback from an environment. The language model is only one part of the agent; orchestration, permissions, tools and stopping conditions are equally important. Large Language Models Mixture-of-Experts Models A mixture-of-experts model contains multiple expert subnetworks and routes each token or input through only a subset of them. This can increase total parameter capacity without activating every parameter on every forward pass. Large Language Models What Is a Large Language Model? A large language model is a statistical model trained on large collections of text or multimodal data to predict and generate token sequences. Modern LLMs are usually transformer-based and are adapted to conversation, coding, tool use and other tasks through post-training and application-level systems. Large Language Models LLM Pretraining LLM pretraining exposes a model to large datasets and optimizes a predictive objective, commonly next-token prediction, before later post-training or adaptation. Large Language Models Fine-Tuning Language Models Fine-tuning continues training a pretrained model on a narrower dataset or objective so it performs better for a particular domain, task or behavior.

Reference guide and primary sources

Wikipedia is used here as a terminology and history reference guide. Current model versions, institutional statistics and product-specific claims are also linked to first-party or institutional sources because those details can change faster than encyclopedia articles.

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