RAG & Vector Search Mastery

Learn how RAG grounds LLM answers with external knowledge. Build a retrieval pipeline with chunking, embeddings, vector search, citations, evaluation, and Python examples.

Learn when a vector database is needed for RAG and semantic search. Compare PostgreSQL with pgvector, search engines, dedicated and managed services; evaluate HNSW, filtering, tenancy, recall, latency, and production cost.

Build semantic search as an authorized, measurable retrieval system. Learn lexical and vector recall, RRF fusion, reranking, index migration, caching, and evaluation with runnable Python examples for RAG, enterprise search, and product discovery.

Build and evaluate a two-stage RAG retrieval pipeline with BM25, dense embeddings, reciprocal-rank fusion, reranking, metadata filters, and latency-aware evaluation.

Reduce unsupported RAG answers with five production controls: source governance, retrieval sufficiency, conflict handling, claim citations, and calibrated abstention.

An engineering guide to graph-based retrieval alongside vector RAG. It explains when graph structure, entity resolution, community summaries, and hybrid retrieval help, where they add cost or risk, and how to build an evaluated, permission-aware pipeline.

Learn how to build knowledge graphs for AI with claim-level provenance, entity resolution, secure graph queries, GraphRAG routing, layered evaluation, and deletion-aware operations.

Compare Retrieval-Augmented Generation (RAG) and Fine-tuning. Discover their differences in cost, hallucination reduction, data updates, and when to use each approach for enterprise AI.

Design and evaluate RAG chunking without relying on universal token sizes or overlap percentages. Compare structural, fixed-token, parent-child, contextual, late, and hierarchical approaches under equal retrieval budgets, with runnable evidence-coverage metrics and production guidance.

Design a production multimodal RAG system for PDFs, charts, images, and text. Compare OCR, captions, shared embeddings, ColPali-style visual retrieval, and hybrid search; then implement routing, rank fusion, evidence packaging, security controls, and layered evaluation.

With Gemini's 2M token context and Claude's 200K, is RAG still necessary? This guide provides a concrete cost-per-query comparison, accuracy benchmarks, and the impact of 2026's Context Caching technology.

Build an AI search engine with query planning, hybrid retrieval, evidence records, claim-level citations, security controls, and measurable release gates.

Design Agentic RAG as a bounded evidence control loop. Compare Self-RAG, CRAG, and Adaptive-RAG; define tool contracts, evaluation gates, and rollback.