Retrieval Augmented Generation (RAG) is one of the most practical AI patterns for real-world applications. It combines the knowledge-retrieval capabilities of search systems with the natural language generation of large language models. Here's how it works, why it matters, and how to build it on AWS and Azure.
What is RAG?
RAG solves a fundamental problem with LLMs: they only know what they were trained on. RAG lets you ground AI responses in your own data by:
- Retrieving relevant documents from a knowledge base using semantic search
- Augmenting the LLM's prompt with those documents as context
- Generating a response that's informed by your specific data
The result: AI that can answer questions about your internal documentation, policies, codebases, or any domain-specific content -- without fine-tuning a model.
How RAG Works Under the Hood
User Question
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Embedding Model (convert question to vector)
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Vector Database (find similar documents)
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Retrieved Documents + Original Question
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LLM (generate contextual answer)
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Response grounded in your data
Building RAG on AWS
Key services:
- Amazon Bedrock -- Managed LLM access (Claude, Titan) with built-in RAG capabilities via Knowledge Bases
- Amazon OpenSearch Serverless -- Vector database for storing and searching document embeddings
- AWS Lambda -- Orchestration layer for the retrieval and generation pipeline
- Amazon S3 -- Document storage for your knowledge base source files
Practical setup:
- Store your documents (PDFs, markdown, HTML) in S3
- Use Bedrock Knowledge Bases to automatically chunk, embed, and index documents into OpenSearch
- Query the knowledge base with natural language -- Bedrock handles retrieval and generation in one API call
Building RAG on Azure
Key services:
- Azure OpenAI Service -- Access to GPT models with your own data
- Azure AI Search (formerly Cognitive Search) -- Vector and hybrid search for document retrieval
- Azure Blob Storage -- Document storage
- Azure Functions -- Serverless orchestration
Practical setup:
- Upload documents to Blob Storage
- Use Azure AI Search indexers to chunk and vectorize content
- Connect Azure OpenAI's "On Your Data" feature to search -- it handles RAG automatically
Real-World Use Cases
Infrastructure Operations -- Index your team's runbooks, postmortems, and architecture docs. Engineers ask questions like "What's our process for scaling the payments service?" and get contextual answers grounded in actual internal documentation.
Compliance and Policy -- Index regulatory requirements and internal policies. Auditors and engineers can query "What are our data retention requirements for PII in us-east-1?" and get specific, sourced answers.
Customer Support -- Index product documentation and known issues. Support agents get AI-powered suggestions that reference actual documentation rather than generic responses.
Developer Onboarding -- New team members query the knowledge base to understand architecture decisions, coding standards, and deployment procedures without interrupting senior engineers.
Key Considerations
- Chunk size matters -- Too large and you lose precision. Too small and you lose context. Start with 500-1000 tokens per chunk with 10-20% overlap.
- Embedding quality -- Use purpose-built embedding models (not general LLMs) for better retrieval accuracy.
- Hybrid search -- Combine vector search with keyword search for best results. Pure semantic search can miss exact terminology.
- Keep sources fresh -- Stale data means stale answers. Automate your indexing pipeline to re-process documents on change.
Getting Started
The fastest path to a working RAG system:
- Pick 50-100 of your most important internal documents
- Use Amazon Bedrock Knowledge Bases or Azure AI Search to index them
- Build a simple query interface (even a Slack bot works)
- Measure answer quality and iterate on chunk size and retrieval parameters
The ROI is immediate -- especially for operations teams drowning in documentation that nobody reads.
Building a RAG pipeline for your team? Let's discuss your architecture.