rag-implementation

Retrieval-augmented generation connects AI models to external knowledge sources via vector databases. Build an ingestion pipeline to embed documents, store them semantically, then retrieve relevant context at query time to generate factual, source-backed responses with reduced hallucinations.

Dense retrieval uses embedding vectors for semantic similarity; sparse retrieval matches keywords and exact terms; hybrid combines both methods. Each strategy offers trade-offs in speed, accuracy, and relevance—hybrid often balances precision and recall across different query types.

Choose embedding models based on domain fit and dimensionality; select vector databases by query latency, storage capacity, and filtering support. Match them to your document volume, retrieval latency requirements, and metadata filtering needs for efficient semantic search.

Yes. RAG systems support multi-source retrieval by ingesting documents from different origins into a single vector store, then applying metadata filters during queries to narrow results by source, date, category, or custom attributes before generating responses.

Document chunking strategies balance context window limits with semantic coherence. Fixed-size chunks with overlap preserve boundaries; recursive chunking respects document structure. Choose based on document type, retrieval granularity, and whether metadata filtering by section or source is needed.

RAG reduces hallucinations by retrieving factual context before generation. Track source metadata (document ID, page, chunk) through retrieval, then include citations in prompts and outputs so generated responses reference and link back to original knowledge sources.