AI Reference 057
Large Language Models
Wikipedia guided
Primary sources linked
Retrieval-Augmented Generation
Definition
Retrieval-augmented generation combines a generative model with retrieval from an external collection such as documents, databases or search results. Retrieved material is inserted into the model's context so the answer can be grounded in information outside the model's parameters.
ATTENTION / CONCEPTUAL WEIGHT MAP
What is RAG?
Retrieval-augmented generation, or RAG, combines a generative model with an external retrieval system. Before answering, the system searches a document collection or database and places relevant information into the model's context.
A typical RAG pipeline
Documents are often split into chunks and indexed with embeddings or keyword search. A user query retrieves the most relevant chunks, and the LLM is instructed to answer using that material. Better systems also keep source identifiers so the answer can show citations.
Why companies use RAG
RAG lets an application use private or frequently changing information without retraining the base model whenever a document changes. Its quality depends heavily on document preparation, retrieval accuracy and whether the model is required to stay within the retrieved evidence.
Related terms, defined
These nearby terms are linked because they name distinct concepts that are easy to confuse with this page's subject.
Large Language Models
Function Calling and Tool Use
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.
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.
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.