What is RAG?
Retrieval-Augmented Generation combines the power of large language models with dynamic knowledge retrieval. Instead of relying solely on training data, RAG systems fetch relevant context at inference time, enabling accurate responses about current information and proprietary knowledge.
Architecture Components
Document Processing
Prepare your knowledge base for retrieval. Chunk documents into appropriate sizes, extract metadata for filtering, and preprocess text for embedding generation. Chunk size significantly impacts retrieval quality—experiment to find optimal settings.
Embedding Generation
Convert text chunks to dense vector representations. Choose embedding models appropriate for your domain—general-purpose models work well broadly, while domain-specific models excel in specialized areas.
Vector Search
Store embeddings in vector databases optimized for similarity search. Implement hybrid search combining semantic similarity with keyword matching. Tune relevance thresholds and result counts for your use case.
Context Construction
Retrieved chunks require assembly into coherent context. Order by relevance, remove redundancy, add source attribution, and format for model consumption. Include chunk boundaries to help models understand context structure.
Evaluation and Tuning
RAG systems require ongoing evaluation. Measure retrieval precision and recall, track answer quality metrics, and continuously tune based on failure analysis. Build feedback loops from user interactions to improve retrieval.
