Key RAG implementation strategies and patterns

Basic RAG Patterns

Naive RAG

• Simple retrieve-then-generate pipeline
• Direct semantic search → context injection → LLM generation
• Works well for straightforward Q&A over documents

Advanced RAG

• Pre-retrieval query optimization (query rewriting, expansion)
• Post-retrieval re-ranking and filtering
• Iterative retrieval with context refinement

Retrieval Strategies

Chunking Approaches

• Fixed-size chunking (simple but can break context)
• Semantic chunking (paragraph/section boundaries)
• Overlapping windows to preserve context
• Hierarchical chunking (summaries + details)

Hybrid Retrieval

• Dense + sparse retrieval (semantic + keyword matching)
• Multiple embedding models for different content types
• Ensemble scoring and result fusion

Query Enhancement

• Query decomposition for complex questions
• Hypothetical document embeddings (HyDE)
• Step-back prompting for broader context
• Multi-query generation and aggregation

Advanced Architectures

Agentic RAG

• Agents that can plan retrieval strategies
• Tool-calling for different knowledge sources
• Self-reflection and retrieval refinement

Graph RAG

• Knowledge graphs + vector search
• Relationship-aware retrieval
• Multi-hop reasoning over connected entities

Conversational RAG

• Context-aware retrieval using conversation history
• Query rewriting based on dialogue context
• Memory management for long conversations

Enterprise Implementation Patterns

Multi-Modal RAG

• Text + image + table retrieval
• Specialized embeddings for different content types
• Cross-modal relevance scoring

Federated RAG

• Retrieval across multiple knowledge bases
• Permission-aware search
• Source attribution and provenance tracking

Real-Time RAG

• Streaming ingestion and index updates
• Cache invalidation strategies
• Incremental knowledge base updates

Optimization Techniques

Retrieval Optimization

• Fine-tuned embedding models for domain data
• Metadata filtering and faceted search
• Approximate nearest neighbor algorithms (FAISS, Pinecone)

Generation Optimization

• Context compression and summarization
• Prompt engineering for better context utilization
• Output validation and fact-checking

System Architecture

• Microservices for scalable components
• Async processing and queue management
• Monitoring and observability for retrieval quality

The choice of strategy depends on your data characteristics, query complexity, latency requirements, and accuracy needs. Most enterprise implementations use a hybrid approach combining multiple strategies.