RAG

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Overview

Retrieval-Augmented Generation (RAG) is a technique that enhances LLM responses by providing relevant context from external knowledge sources. Instead of relying solely on training data, RAG systems retrieve and incorporate up-to-date information at inference time.

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Core Components

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1. Document Processing

• Chunking: Breaking documents into semantically meaningful segments
• Preprocessing: Cleaning and normalizing text for optimal retrieval
• Metadata Extraction: Preserving source, date, and structural information

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2. Embedding Generation

• Vector Representations: Converting text chunks into high-dimensional vectors
• Embedding Models: Using specialized models (e.g., text-embedding-ada-002, all-MiniLM-L6-v2)
• Dimensionality: Balancing between representation quality and computational efficiency

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3. Vector Storage

• Vector Databases: Purpose-built stores like Pinecone, Weaviate, or Qdrant
• Indexing Strategies: HNSW, IVF, or LSH for efficient similarity search
• Hybrid Search: Combining vector similarity with keyword matching

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4. Retrieval Strategies

• Similarity Search: Finding the most relevant chunks based on cosine similarity
• Reranking: Using cross-encoder models to refine initial results
• Query Expansion: Enhancing user queries for better retrieval

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5. Context Integration

• Prompt Engineering: Effectively presenting retrieved context to the LLM
• Context Window Management: Optimizing the amount of context within token limits
• Source Attribution: Maintaining references to original documents

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Common RAG Patterns

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Basic RAG

1. Embed user query
2. Search vector database for similar chunks
3. Inject top-k results into LLM prompt
4. Generate response with retrieved context

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Advanced Techniques

• Multi-hop Retrieval: Iterative retrieval based on intermediate results
• Dense-Sparse Hybrid: Combining embedding search with BM25 keyword search
• Query Decomposition: Breaking complex queries into sub-questions
• Contextual Compression: Summarizing retrieved chunks to fit more information

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In Civic Labs

RAG principles power our Civic Knowledge system, enabling AI assistants to access and reason over organizational data while maintaining security and access controls. Our implementation focuses on:

• Secure Retrieval: Respecting document permissions and access controls
• Multi-source Integration: Unified search across diverse data sources
• Real-time Updates: Keeping knowledge bases current without retraining

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Best Practices

1. Chunk Size Optimization: Balance between context and relevance
2. Embedding Model Selection: Match model to your domain and use case
3. Metadata Filtering: Use structured data to improve retrieval precision
4. Evaluation Metrics: Monitor retrieval quality and generation accuracy
5. Fallback Strategies: Handle cases when retrieval returns no relevant results

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Learn More

• Civic Knowledge - Our RAG-powered AI assistant
• MCP Integration - How MCP enables secure data access for RAG