Retrieval-Augmented Generation and Knowledge Systems

What is RAG?

RAG stands for retrieval-augmented generation. It retrieves information relevant to a question from external sources and supplies it to an LLM so the model can answer with more complete context.

• Retrieval and preprocessing: query websites, knowledge bases or databases, then tokenize, stem and remove words unnecessary for retrieval.
• Grounded generation: integrate retrieved information into a pretrained LLM to improve contextual understanding and produce more precise, useful responses.

How has it developed?

By the first half of 2026, RAG has evolved from fixed Naive RAG workflows to Modular RAG, which combines retrieval, routing, reranking and verification modules. Developers select retrieval strategies for their data, use cases and accuracy requirements to build more flexible systems.

Why use it?

Hallucinations and outdated information are common issues in LLM adoption. RAG organizes enterprise data into searchable knowledge bases, enabling answers grounded in relevant information for employee training, customer support and internal knowledge management, and reducing time spent searching.