RAG Pipeline

Definition

A RAG (Retrieval-Augmented Generation) Pipeline combines document retrieval with large language models to ground AI responses in specific data. Instead of relying solely on model training data, RAG retrieves relevant documents from a knowledge base and uses those documents to answer questions. This prevents hallucinations (AI making up false facts) and keeps responses grounded in your proprietary data.

How RAG Works on AWS

Step 1: Document Ingestion

• Upload documents (PDFs, text, HTML) to S3
• Use Amazon Textract to extract text from documents
• Split documents into chunks (300-500 word sections)

Step 2: Embedding & Storage

• Convert document chunks to embeddings (numerical vectors) using Amazon Bedrock embeddings
• Store embeddings in a vector database (Pinecone, Weaviate, or Amazon OpenSearch with vector search)
• Build a searchable index of your knowledge base

Step 3: Query & Retrieval

• User asks a question
• Convert question to embedding using same model as Step 2
• Retrieve top-k most similar documents from vector database

Step 4: Generation

• Pass retrieved documents + user question to Bedrock Claude/Haiku model
• Model generates answer grounded in retrieved documents
• Response includes citations/references to source documents

Common Mistakes

Mistake 1: Using RAG on unstructured, low-quality documents. If your knowledge base is poor, RAG outputs will be poor.

Mistake 2: Embedding entire documents at once instead of chunking. Large documents dilute the relevance signal.

Mistake 3: Not re-embedding when documents change. Stale embeddings retrieve wrong documents.

Related AWS Services

• Amazon Bedrock: Provides embedding models and Claude for generation
• Amazon OpenSearch with Vector Search: Scales to millions of document chunks
• Amazon Textract: Extracts text from PDFs and scanned documents
• Amazon S3: Storage for document corpus

Related FactualMinds Content

• Fine-Tuning vs RAG on AWS Bedrock: When to Use Each
• Generative AI on AWS Bedrock for Enterprises