RAG & Vector Search Mastery

Understand RAG through document ingestion, retrieval, permissions, citations, and evaluation, with a runnable Go example showing how evidence reaches an answer.

Learn when RAG needs a vector database. Compare pgvector, search engines, dedicated systems, HNSW, filtering, benchmarks, migration, and total cost in practice.

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 hybrid RAG retrieval with BM25, dense search, RRF, and reranking. Learn candidate budgets, access filters, evaluation, latency, and release gates.

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.

Build production knowledge graphs for AI with claim provenance, entity resolution, secure GraphRAG routing, layered evaluation, and deletion-aware operations.

Compare RAG, fine-tuning, and long context as measurable system choices. Learn how knowledge freshness, behavior adaptation, retrieval quality, latency, cost, deletion, and evaluation determine the right architecture.

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.

Choose between long context, prompt caching, RAG, and a hybrid architecture with a workload-driven method. This guide replaces fixed token thresholds and universal cost claims with measurable criteria for evidence coverage, freshness, access control, cache reuse, latency, answer quality, citations, and operational complexity.

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.