The Complete Guide to RAG for Enterprise

Every enterprise AI project eventually hits the same wall: the model doesn't know your business. It doesn't know your internal policies, your product documentation, your customer history, or the institutional knowledge locked inside your organisation. Retrieval-Augmented Generation — RAG — is the architecture that solves this problem.

What is RAG?

RAG is a two-step process. First, when a user asks a question, the system retrieves the most relevant documents from your knowledge base. Second, those documents are injected into the LLM's context window alongside the question. The model then generates an answer grounded in your actual data — not its training data.

This is fundamentally different from fine-tuning. Fine-tuning bakes knowledge into the model weights — an expensive, slow process that becomes stale the moment your documents change. RAG retrieves knowledge at query time, so your AI is always working with the latest information.

The RAG Pipeline: Step by Step

1. Document Ingestion

Documents are loaded from your sources — SharePoint, Confluence, S3, databases, PDFs — and split into chunks. Chunk size matters enormously. Too small and you lose context. Too large and you waste the context window and reduce retrieval precision. For most enterprise use cases, 512–1024 tokens with 10–20% overlap works well.

2. Embedding

Each chunk is converted into a vector embedding — a numerical representation of its semantic meaning. Similar chunks will have similar vectors. We use models like text-embedding-3-large from OpenAI or open-source alternatives like BGE-M3 for air-gapped deployments.

3. Vector Storage

Embeddings are stored in a vector database. For enterprises already running PostgreSQL, pgvector is the simplest path — no new infrastructure. For larger scale or more advanced filtering, Weaviate or Qdrant offer more features.

4. Retrieval

At query time, the user's question is embedded and compared against stored vectors using cosine similarity. The top-k most similar chunks are retrieved. We recommend hybrid retrieval — combining vector search with BM25 keyword search — for the best results.

5. Generation

Retrieved chunks are assembled into a prompt with the user's question and sent to the LLM. The model generates an answer citing the source documents. Source citations are non-negotiable in enterprise deployments — users need to verify AI responses.

Common RAG Failure Modes

• →Retrieval misses: The right document exists but isn't retrieved. Fix with hybrid search and re-ranking.
• →Context overflow: Too many chunks exceed the context window. Fix with better chunk selection and summarisation.
• →Stale embeddings: Documents updated but embeddings not refreshed. Fix with incremental re-indexing pipelines.
• →Access control gaps: Users retrieve documents they shouldn't see. Fix with metadata filtering at retrieval time.
• →Hallucination on gaps: Model invents answers when no relevant chunk is found. Fix with explicit 'I don't know' instructions in the system prompt.

RAG vs Fine-tuning: When to Use Each

Use RAG when your knowledge changes frequently, when you need source citations, when you have large document volumes, or when you need access control at the document level. Use fine-tuning when you need the model to adopt a specific tone or format, or when you're teaching it a new task rather than new knowledge. In most enterprise deployments, RAG is the right starting point.

Getting Started

Start small. Pick one high-value knowledge source — your HR policy documents, your product FAQ, your engineering runbooks — and build a RAG pipeline around it. Measure retrieval quality with a small set of test questions. Iterate on chunk size, embedding model, and retrieval strategy before scaling to your full knowledge base.