The first stage of RAG involves loading and preprocessing documents from various sources. Document loaders handle multiple formats including PDF, HTML, Markdown, and code files. Preprocessing includes cleaning text, normalizing formats, and enriching content with metadata. Effective ingestion sets the foundation for high-quality retrieval by ensuring documents are properly structured and tagged with relevant metadata like titles, authors, dates, and source information.

Transform document chunks into dense vector representations that capture semantic meaning. Embedding models range from lightweight Sentence Transformers (384D) to powerful OpenAI embeddings (1536D) and Cohere (3072D). Model selection depends on accuracy requirements, latency constraints, and whether multilingual or domain-specific capabilities are needed. Batch processing enables efficient bulk embedding of large document collections.

Store and index embeddings in specialized vector databases optimized for similarity search. Vector databases like Pinecone, Weaviate, and Qdrant provide efficient nearest-neighbor search using indexing algorithms like HNSW or IVF. Metadata filtering enables pre-filtering or post-filtering to narrow results based on structured attributes. Namespace organization supports multi-tenancy and versioning for production deployments.

Query the vector database to find relevant document chunks for a given user question. Search strategies include pure similarity search, Maximum Marginal Relevance (MMR) for diversity, and hybrid search combining semantic and keyword matching. Re-ranking with cross-encoders refines initial results. Query transformation techniques like decomposition and rewriting improve retrieval quality. Result fusion algorithms like Reciprocal Rank Fusion combine multiple retrieval strategies.

Format retrieved documents into context suitable for LLM consumption while staying within token limits. Result formatting optimizes context window usage by prioritizing most relevant information. Source citation tracks document provenance for transparency and verification. Context compression removes redundant or irrelevant sentences to fit more useful information. Prompt engineering constructs effective system and user messages that guide the LLM to generate accurate, grounded responses.

Use an LLM to synthesize retrieved context into a coherent answer addressing the user's question. Model selection balances cost, latency, and quality requirements across options like GPT-4, Claude, or open-source Llama models. Prompt strategies like few-shot learning and chain-of-thought reasoning improve answer quality. Streaming provides token-by-token responses for better user experience. Answer synthesis involves multi-document reasoning to combine insights from multiple sources into comprehensive responses.

Generate multiple search queries from a single user question to improve retrieval coverage and reduce missed context.

Model critiques and refines its own retrieval and generation through reflection, improving answer quality iteratively.

Autonomous agents decide when and how to retrieve information, using tools and reasoning to answer complex questions.

Evaluates retrieval quality and corrects by re-retrieving, web search, or generation fallback for better results.

Combines knowledge graphs with vector search for structured relationship understanding and multi-hop reasoning.

Combines multiple retrieval methods (vector, keyword, metadata) and fuses results using algorithms like RRF.

Fully managed RAG service connecting foundation models to private data sources. Handles ingestion, chunking, embedding, vector storage, retrieval, and prompt augmentation end-to-end with built-in citations.

Microsoft's managed search service with vector search, semantic ranking, and integrated skills for document cracking, OCR, and entity extraction. Deep Azure OpenAI integration.

Google Cloud's enterprise search and RAG platform with grounding in Google Search, website indexing, and unstructured document understanding.

Cohere's retrieval-augmented generation with Command R models optimized for RAG, built-in web search connector, and enterprise connectors for common data sources.