What is RAG? - Explanation & Meaning

Definition

Retrieval-Augmented Generation (RAG) is an AI architecture pattern that supplements a large language model's knowledge by retrieving relevant information from external data sources before generating a response, reducing hallucinations and grounding output in current, verifiable data.

Technical explanation

RAG operates in three phases: indexing, retrieval, and generation. During indexing, documents are split into chunks, converted to numerical vector representations (embeddings) via an embedding model, and stored in a vector database such as Pinecone, Weaviate, or pgvector. When a user query arrives, it is likewise converted to an embedding, and the most relevant chunks are retrieved via similarity search (typically cosine similarity or dot product). These chunks are provided as context to the LLM, which generates a response based on both its trained knowledge and the retrieved information. Advanced RAG implementations in 2026 include hybrid search (combining vector and keyword search), re-ranking models that improve retrieved chunk relevance, query expansion that enriches the original question, and agentic RAG where an AI agent dynamically decides which data sources to consult. Chunking strategies range from fixed sizes to semantic chunking that respects natural document boundaries.

How MG Software applies this

RAG is the standard architecture at MG Software for all AI solutions requiring business-specific knowledge. We build RAG pipelines that unlock internal documents, knowledge bases, and databases via AI. Our implementations use hybrid search, re-ranking, and evaluation metrics to continuously monitor and improve answer quality.

Practical examples

• An HR department implementing a RAG system that allows employees to ask natural language questions about company policies, benefits, and procedures, with answers always referencing the actual source documents.
• A technical support team deploying RAG to make a knowledge base of thousands of product manuals and troubleshooting guides searchable via an AI chatbot, reducing average resolution time by 45%.
• A legal department using RAG to retrieve relevant case law and statutory articles when drafting contracts, ensuring attorneys always work with the most current regulations.

Related terms