What is RAG (Retrieval Augmented Generation)? - Definition & Meaning

Definition

RAG (Retrieval Augmented Generation) is an AI architecture that combines Large Language Models with a retrieval system. Before the model answers, relevant information is first retrieved from documents or databases, enabling the AI to provide accurate, grounded answers instead of relying solely on training data.

Technical explanation

A RAG pipeline consists of: 1) document chunking and embedding (converting to vector representations), 2) storage in a vector database, 3) similarity search on the user question to find relevant chunks, 4) injecting those chunks as context into the LLM prompt. Popular tools include LangChain, LlamaIndex, Pinecone, Weaviate, and pgvector. RAG reduces hallucinations, enables using business knowledge without fine-tuning, and supports source attribution. Hybrid search (combining vector and keyword search) often improves relevance.

How AVARC Solutions applies this

AVARC Solutions builds RAG systems for clients who want to deploy AI on their own data. From knowledge bases and documentation assistants to customer service with up-to-date product information, we integrate RAG so LLMs answer accurately and traceably based on your sources.

Practical examples

• An internal knowledge base where employees ask questions and the AI answers based on company documents, manuals, and policy papers.
• A customer service chatbot that retrieves product information, FAQs, and recent updates and passes them to the LLM with the question for accurate answers.
• A legal research assistant that retrieves precedents and legislation and provides them to the model with the question for well-founded analysis.

Related terms