RAG Series Part 2: How RAG Works - The Technical Architecture

RAG Blog Series: Complete Guide to Retrieval-Augmented Generation

Series Overview

This 5-part blog series provides a comprehensive guide to Retrieval-Augmented Generation (RAG), from basic concepts to advanced implementations. Each post builds upon the previous one, making complex AI concepts accessible to both technical and non-technical readers.

Part 2: How RAG Works - The Technical Architecture

Part 2 of 5

Now that we understand what RAG is and why it's important, let's explore the technical architecture that makes it all possible. Don't worry – we'll keep it accessible while diving into the fascinating details of how RAG systems operate.

The RAG Architecture: Key Components

1. Document Processing Pipeline

Document Ingestion

• Raw documents (PDFs, websites, databases) are collected
• Various formats are normalized into a standard structure
• Metadata is extracted and preserved

Text Chunking

• Large documents are broken into smaller, manageable pieces
• Optimal chunk size balances context and specificity
• Overlapping chunks ensure important information isn't lost

Preprocessing

• Text is cleaned and formatted
• Special characters and formatting are handled
• Quality filtering removes irrelevant content

2. Vector Embeddings: The Heart of RAG

What are Embeddings? Vector embeddings convert text into numerical representations that capture semantic meaning. Similar concepts end up close together in this mathematical space.

The Embedding Process:

1. Each text chunk is processed by an embedding model
2. The model converts text into a high-dimensional vector (typically 768 or 1536 dimensions)
3. These vectors capture the meaning and context of the text
4. Similar content produces similar vectors

Why This Matters:

• Enables semantic search (finding meaning, not just keywords)
• Allows the system to find related concepts even with different wording
• Powers the core retrieval mechanism

3. Vector Database: The Knowledge Store

Storage Structure

• Vectors are stored in specialized databases optimized for similarity search
• Popular options include Pinecone, Weaviate, Chroma, and FAISS
• Each vector is linked to its original text and metadata

Indexing for Speed

• Advanced indexing techniques (like HNSW) enable fast similarity search
• Trade-offs between search speed and accuracy
• Horizontal scaling for large knowledge bases

4. Retrieval System: Finding Relevant Information

Query Processing

1. User query is converted to a vector using the same embedding model
2. Vector database performs similarity search
3. Top K most similar chunks are retrieved
4. Results are ranked by relevance score

Hybrid Search Approaches

• Semantic Search: Vector similarity for meaning
• Keyword Search: Traditional text matching
• Hybrid: Combines both approaches for better results

Retrieval Strategies

• Dense Retrieval: Uses vector similarity exclusively
• Sparse Retrieval: Uses keyword matching
• Hybrid Retrieval: Combines multiple methods

5. Generation Component: Creating the Response

Context Assembly

• Retrieved chunks are assembled into a coherent context
• System prompts guide the AI's behavior
• Token limits are managed to fit within model constraints

Response Generation

• Large language model (like GPT, Claude, or Llama) generates response
• Model uses both its training knowledge and retrieved context
• Generation is guided by specific prompts and parameters

Post-processing

• Responses are formatted and cleaned
• Citations and source references are added
• Quality checks and filtering are applied

The RAG Workflow: Step by Step

Preparation Phase (Offline)

1. Data Collection: Gather documents and data sources
2. Processing: Clean, chunk, and preprocess content
3. Embedding: Convert chunks to vectors
4. Storage: Store vectors and metadata in database
5. Indexing: Create efficient search indexes

Query Phase (Real-time)

1. Query Reception: User submits question
2. Query Embedding: Convert query to vector
3. Similarity Search: Find relevant chunks
4. Context Preparation: Assemble retrieved content
5. Generation: AI creates response using context
6. Response Delivery: Return answer with citations

Advanced RAG Techniques

Metadata Filtering

• Filter search results by document type, date, author, etc.
• Enables more targeted retrieval
• Improves relevance and reduces noise

Reranking

• Second-stage ranking of retrieved results
• Uses more sophisticated models to improve relevance
• Balances computational cost with accuracy

Query Expansion

• Automatically expand queries with synonyms or related terms
• Improves recall for complex queries
• Handles vocabulary mismatches

Multi-hop Reasoning

• Enables reasoning across multiple retrieved chunks
• Useful for complex questions requiring multiple sources
• Chains together related information

Performance Considerations

Latency Factors

• Embedding generation time
• Vector search speed
• Language model inference time
• Network and I/O overhead

Scalability Challenges

• Vector database size and performance
• Concurrent user handling
• Cost optimization strategies

Quality Metrics

• Retrieval accuracy (precision and recall)
• Response relevance and correctness
• Source attribution accuracy
• User satisfaction scores

Coming Up Next

In Part 3, we'll explore the different types of RAG systems and their specific use cases, helping you understand which approach might be best for your particular needs.