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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