9 docs tagged with "rag-engineering"

Build RAG systems that reason, iterate, and self-correct - covering Self-RAG, FLARE, ReAct tool-augmented RAG, RAPTOR, and Corrective RAG with full production implementations using the Anthropic SDK.

Master every chunking strategy from fixed-size to semantic and structure-aware splitting. Learn how to parse PDFs, DOCX, and HTML, enrich metadata, evaluate chunk quality, and build a production-grade ingestion pipeline.

How to choose, deploy, and manage embedding models at scale - including versioning, caching, batching, and migration strategies for production RAG systems.

How to combine BM25 sparse retrieval with dense vector search using Reciprocal Rank Fusion, and how to apply cross-encoder reranking for precision that neither method achieves alone.

Master query transformation techniques - HyDE, multi-query retrieval, step-back prompting, query decomposition, and routing - to solve the vocabulary mismatch problem that breaks naive RAG systems in production.

Build production-grade Retrieval-Augmented Generation systems - from document ingestion and chunking through hybrid search, query transformation, agentic RAG, and RAGAS evaluation.

Build a continuous RAG evaluation pipeline using the RAGAS framework - faithfulness, answer relevance, context precision, and context recall - with full production implementations using the Anthropic SDK and automated regression detection.

How approximate nearest neighbor search works, how to choose the right vector database, and how to build production-grade retrieval pipelines that stay fast at millions of documents.

Understand why Retrieval-Augmented Generation was invented, what problems it solves that fine-tuning and prompt stuffing cannot, and how to architect a minimal RAG pipeline from scratch.