Retrieval-Augmented Generation (RAG) Market Size, Share, By Component [Solution (RAG platforms, Vector databases, and Others), Services (Deployment & Integration Services, Consulting & System Design, and Others)], Technology (Traditional RAG, Advanced / Modular RAG, Multimodal RAG, and Others), Application (Customer Support Automation, Knowledge Management & Enterprise Search, Document Summarization & Analytics, and Others), End User (IT & Telecom, BFSI, Healthcare & Life Sciences, Retail & E-commerce, and Others), and Region - Trends, Analysis, and Forecast till 2036

Retrieval-augmented generation (RAG) market size was valued at USD 2.69 Billion in 2026 and is expected to reach USD 72.6 Billion by 2036, growing at a CAGR of 39%

An AI system called retrieval-augmented generation (RAG) improves the efficiency of big language models by retrieving the most recent and pertinent data from external knowledge sources (such as databases, documents, or the internet) before producing a response.

The market for retrieval-augmented generation (RAG) is growing quickly as businesses need AIs that can leverage genuine internal knowledge and have accuracy and low hallucinations. RAG is currently widely used in businesses to enhance decision-making, automate knowledge-intensive processes, and provide customer care at a lower cost than retraining big models.