End-to-End RAG Workflow: How Retrieval Augmented Generation Works

End-to-End RAG Workflow: How Retrieval Augmented Generation Works | Databricks Blog Skip to main content

Summary

Retrieval Augmented Generation (RAG) connects large language models to external knowledge bases through a five-stage pipeline — ingestion, embedding, retrieval, augmentation, and generation — enabling accurate, domain-specific answers without retraining the model.

A production RAG workflow requires selecting the right embedding model, configuring vector database indexing and chunking strategies, and implementing hybrid search that combines semantic vector search with keyword fallback to maximize retrieval quality.

RAG evaluation must measure retrieval precision and generation faithfulness independently, because strong LLM performance cannot compensate for a weak information retrieval component, and continuous data updates are essential to prevent stale knowledge from degrading response accuracy.

Retrieval Augmented Generation (RAG) is an AI architecture pattern that connects large language models to external knowledge sources at inference time, enabling those models to generate accurate, context-aware responses that go beyond their static training data. Rather than relying on knowledge encoded during pretraining, a RAG system retrieves relevant documents from an external database in response to each user query and injects that content into the LLM prompt before generation. The result is a generative AI system that produces accurate, domain-specific answers grounded in verified sources — without requiring full model retraining every time the underlying knowledge changes. LLMs often provide outdated answers due to knowledge cutoffs and cannot access proprietary internal documents or real-time external data sources. RAG directly addresses this limitation. Over 60% of organizations are actively developing AI-powered retrieval tools, reflecting a fundamental shift from relying solely on model memory to dynamically connecting AI to live knowledge bases containing internal documents, product documentation, and current data. This guide walks through the complete RAG workflow — from architecture components and data ingestion to hybrid retrieval, prompt design, evaluation, and deployment — with practical guidance for teams building production RAG pipelines. Key Components of a RAG Architecture RAG systems contain four primary components: a knowledge base that stores external knowledge, an information retrieval component (the retriever) that finds relevant documents for each query, an integration layer that assembles retrieved context into an LLM prompt, and a generator (the LLM) that produces the final response. Each component can be optimized independently, and overall pipeline quality is bounded by the weakest link — a high-quality LLM cannot compensate for a retriever that consistently surfaces irrelevant documents. The Retriever and Vector Database The retriever accepts a user query, converts it into a comparable representation, and returns the most relevant documents from the knowledge base. Retriever quality is the single biggest determinant of RAG output quality. The vector database stores numerical representations of document chunks — called embeddings — enabling fast similarity search at scale. Unlike relational databases that match on exact values, vector databases find documents whose meaning is semantically closest to the query using distance metrics like cosine similarity. The Generator and Orchestration Layer The generator is the large language model that receives the augmented prompt — the user's original question combined with retrieved context — and produces the final response. The orchestration layer connects all components into a coherent rag pipeline, handling prompt assembly, conversation history, and error handling. Frameworks like LangChain and LlamaIndex provide common orchestration primitives, while platforms like Databricks deliver managed infrastructure for the full stack. Data Sources and External Knowledge The range of valid data sources for a RAG system is broad: structured data in relational tables, unstructured text in PDFs and markdown files, internal documents like engineering runbooks and HR policies, product documentation, and external knowledge bases. Domain-specific data — content directly relevant to the questions users will ask — should be ingested first and maintained most carefully. Internal data, including proprietary research and internal documents, generates the most defensible advantage in a RAG implementation because it represents knowledge no public LLM was trained on. The practical question when selecting data sources is relevance density: what percentage of indexed documents will actually be retrieved in response to real queries? High-relevance sources justify the computational and financial costs of embedding and indexing; low-relevance sources dilute retrieval quality by increasing the noise the retriever must filter. Multiple data sources can be combined in a single RAG system — for example, pairing a product documentation corpus with a real-time customer database — as long as the ingestion pipeline normalizes each source to a consistent text format. Teams should document the data lineage of every indexed source so that the origin of any retrieved document can be traced back to its authoritative origin, enabling audit and compliance workflows in regulated industries. Embedding Model and Vector Store Selecting an Embedding Model An embedding model is a specialized language model that converts text into numerical representations — high-dimensional vectors that encode semantic meaning. When a user submits a query, the same embedding model converts that user input into a comparable vector, enabling mathematical comparison between the query and all stored document embeddings. The embedding model used during ingestion must be identical to the one used at query time. Model selection involves tradeoffs between representation quality, vector dimensionality, inference latency, and financial costs. General-purpose models like bge-large-en produce 1,024-dimension vectors that perform across diverse domains. Domain-specific embedding models fine-tuned on technical text often outperform general models in narrow retrieval tasks. Embedding models transform raw text into the numerical representations that make vector similarity search possible. Embedding models can also be evaluated on their ability to handle queries that are...