What is Retrieval-Augmented Generation (RAG)?

What is Retrieval-Augmented Generation (RAG)?

Retrieval-Augmented Generation (RAG) is an architectural pattern that grounds Large Language Models (LLMs) in retrieved documents, often proprietary enterprise data. Introduced in a 2020 paper by researchers at Facebook AI Research (now Meta AI), University College London, and New York University, RAG addresses two major limitations of raw LLMs: they have no access to private corporate data, and they can hallucinate (confidently invent) facts when they do not know an answer.

A raw LLM is a snapshot in time, trained on large amounts of mostly public data up to a cutoff date. If a CEO asks a raw LLM, “What were our company’s exact internal sales figures for last week?”, the LLM cannot answer accurately because that specific proprietary data was never included in its training set. If pressed to answer, the LLM may produce a plausible but fabricated number. RAG changes this process by retrieving relevant internal documents and giving them to the LLM to read before it generates its response.

The Architecture of RAG Pipelines

A production RAG pipeline consists of two decoupled phases: the Ingestion Pipeline (the preparation of the data) and the Retrieval/Generation Pipeline (the real-time user interaction).

Phase 1: The Ingestion Pipeline

To allow an AI to search proprietary data, the data must first be converted into a format the AI understands.

1. Extraction: A data pipeline extracts raw unstructured data (PDFs, Confluence pages, Slack messages) from the corporate data lakehouse.
2. Chunking: LLMs have strict context window limits. The pipeline splits large documents into smaller, logical chunks (e.g., a few hundred words each).
3. Embedding: The pipeline passes each chunk through an Embedding Model (like OpenAI’s text-embedding-3-small). The model converts the text chunk into a high-dimensional vector that represents its meaning.
4. Storage: The pipeline stores the vectors, alongside the original raw text and critical metadata (like the document_id and access_level), into a Vector Database (like Pinecone or Milvus).

Phase 2: The Retrieval and Generation Pipeline

When an employee asks a question through a corporate chatbot, the RAG architecture activates:

1. Query Embedding: The framework (often built using LangChain or LlamaIndex) converts the employee’s natural language question into a vector using the same Embedding Model.
2. Semantic Search: The framework runs a similarity search against the Vector Database, which returns the top matching chunks (for example, the top 5).
3. Prompt Augmentation: The framework builds a new prompt that combines the original question, system instructions, and the retrieved text chunks.
4. Generation: The framework sends this augmented prompt to the LLM. The system prompt typically instructs the LLM: “Answer the user’s question using ONLY the provided context. If the answer is not in the context, explicitly state that you do not know.”

Reducing Hallucinations and Enforcing Security

By asking the LLM to act as a reading comprehension engine rather than a knowledge recall engine, RAG reduces hallucinations, though it does not remove them, and answers are only as good as the retrieved chunks. Because the answer draws on specific retrieved chunks, the RAG application can show the user links back to the original source documents so people can check the answer.

RAG also fits enterprise security models better than fine-tuning on sensitive data. A company should not fine-tune an LLM on all of its proprietary data, because an intern might ask the LLM “What is the CEO’s salary?” and the LLM might answer based on its training. In a RAG architecture, the retrieval step can apply Metadata Filtering based on the user’s permissions. If the intern lacks the Role-Based Access Control (RBAC) clearance to view HR documents, the Vector Database does not return those chunks, so the LLM never receives that data. This only works if the permission metadata is accurate and the filter is applied on every query.

Summary of Technical Value

Retrieval-Augmented Generation (RAG) is one of the most common patterns for deploying LLMs in the enterprise. By injecting relevant, permission-filtered data into the context window of a Large Language Model at runtime, RAG reduces the need to fine-tune models on proprietary facts (fine-tuning is still useful for style, format, and specialized tasks). It reduces hallucinations by grounding the model’s answers in retrieved company documents.

Learn More

To learn more about the Data Lakehouse, read The Lakehouse Built for Everyone by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.