RAG (Retrieval-Augmented Generation)

The architecture that makes AI trustworthy — by grounding large language models in your organisation's own data.

Over 70% of enterprise generative AI deployments in 2026 incorporate Retrieval-Augmented Generation, making it the most widely adopted architecture for production AI systems. RAG addresses the fundamental limitation of large language models: they generate confident but sometimes fabricated answers based on training data rather than factual, current information. For business leaders, RAG is the difference between an AI that hallucinates and an AI that cites its sources — the difference between a tool that is a liability and one that is an asset.

What It Is

Retrieval-Augmented Generation, or RAG, is an AI architecture that combines a large language model with a retrieval system that fetches relevant documents from a knowledge base before the model generates a response. In simple terms, instead of asking the AI to answer from memory (which may be outdated or incorrect), RAG first searches your organisation's documents for relevant information, feeds that information to the AI model alongside the user's question, and asks the model to generate its answer based on the retrieved documents.

Think of it this way: asking a large language model a question without RAG is like asking a brilliant colleague to answer from memory — they will give you a confident answer, but it may contain inaccuracies or outdated information. Using RAG is like handing that same colleague a stack of relevant documents and asking them to answer based on what is in those documents. The colleague's skill lies in reading, synthesising, and communicating the information clearly, but the factual foundation comes from the documents you provided. This dramatically reduces the risk of hallucination — the model making things up — because it is explicitly instructed to base its response on retrieved content.

The technical architecture has three components: a knowledge base (typically a vector database containing embeddings — mathematical representations — of your documents), a retrieval system (which searches the knowledge base for documents relevant to the user's query), and a large language model (which generates a natural language response based on both the query and the retrieved documents). Modern RAG systems have evolved to include sophisticated features: hybrid search (combining keyword and semantic search), re-ranking (ordering retrieved documents by relevance), citation tracking (linking generated text to specific source documents), and multi-modal retrieval (searching across text, images, and tables).

Why It Matters for Business

RAG transforms generative AI from a general-purpose tool into an organisation-specific knowledge system. Without RAG, a large language model knows what is publicly available on the internet — general knowledge, public reports, common practices. With RAG, the same model can answer questions based on your company's policies, contracts, product manuals, financial reports, customer histories, and internal procedures. This is what makes AI useful for enterprise applications: an AI that knows your business, not just the world in general.

The business impact is measurable. Companies implementing RAG-powered knowledge assistants report 30-50% reductions in time employees spend searching for information, 20-40% improvements in customer support first-contact resolution rates, and significant reductions in compliance risk. For regulated industries — financial services, healthcare, legal — RAG is essential because it enables AI systems that can cite their sources, providing the auditability that regulators require. A RAG-powered system can show exactly which document and passage it used to generate a response, enabling human reviewers to verify accuracy.

Cost is another significant advantage. Fine-tuning a large language model on your proprietary data — the alternative approach to customisation — can cost $100,000-500,000 per model and requires repeating whenever your data changes. RAG simply retrieves from your current document store, meaning it always has access to the latest information with no retraining costs. For an organisation with a document corpus that changes frequently — think of a law firm updating case files, or a pharmaceutical company updating drug information — RAG is both more cost-effective and more accurate than fine-tuning.

Enterprise Use Cases

Nous Research implemented a RAG-powered legal research assistant for a top-ten global law firm that indexes over 5 million legal documents, case files, and regulatory submissions. Lawyers can ask natural-language questions and receive synthesised answers with citations to specific paragraphs in specific documents. The system reduced average legal research time from 4 hours to 15 minutes per query, and the citation feature means lawyers can immediately verify the AI's output against source material — critical for maintaining professional liability standards. The firm estimates the system saves $12 million annually in billable hours previously spent on manual research.

NVIDIA uses RAG internally for its technical support operations, feeding the system with product documentation, bug reports, and resolved support tickets. Customer support engineers ask questions and receive answers grounded in verified technical documentation, reducing average resolution time by 35% and improving first-contact resolution rates by 25%. The system handles over 50,000 queries per month from NVIDIA's global support team. In financial services, JPMorgan Chase deployed a RAG system called LLM Suite for its 60,000 banking employees, enabling them to query internal research, regulatory guidance, and client information securely. The system accesses over 100 million internal documents whilst maintaining strict access controls so employees only see information they are authorised to access.

In healthcare, Mayo Clinic built a RAG-powered clinical decision support system that retrieves relevant medical literature, treatment guidelines, and patient history when clinicians ask questions about patient care. The system serves over 10,000 clinicians and has been shown to improve adherence to evidence-based treatment protocols by 15%, whilst the citation feature enables clinicians to immediately verify recommendations against primary sources.

Vendor Landscape

The RAG ecosystem spans vector databases, retrieval services, and end-to-end platforms. Vector databases — the specialised data stores that power RAG — include Pinecone (the market leader, with pricing from $70 per month to enterprise contracts exceeding $200,000 annually), Weaviate (open-source and commercial), Milvus (open-source), Qdrant (open-source and cloud), and pgvector (an extension for PostgreSQL that enables vector search in existing databases). Cloud providers offer integrated RAG services: AWS Bedrock Knowledge Bases, Azure AI Search with vector capabilities, and Google Cloud's Vertex AI Search all provide managed RAG with minimal setup. End-to-end RAG platforms include Cohere (enterprise pricing from approximately $5,000 per month), Glean (focused on enterprise search, with annual contracts typically $200,000-500,000), and Contextual AI. Open-source frameworks like LangChain and LlamaIndex provide the building blocks for custom RAG implementations, with active communities and extensive documentation.

Strategic Considerations

Data quality is the single most important factor in RAG success — the system can only retrieve what exists and is well-structured in your knowledge base. Invest in data preparation, including cleaning, deduplicating, and properly chunking documents before indexing. Consider access control carefully: RAG systems must respect existing document permissions, so users only receive answers based on documents they are authorised to see. Implement robust evaluation frameworks that measure not just the quality of generated text but the accuracy of retrieval — the best AI response is worthless if it is based on the wrong documents. Start with a focused pilot on a well-defined use case (internal knowledge base, customer support, or compliance queries) rather than attempting to index your entire enterprise data estate at once. Finally, plan for ongoing maintenance: document collections grow and change, and your RAG system needs automated ingestion pipelines that keep the knowledge base current without manual intervention.