A deep technical series on RAG and vector retrieval systems, covering vector databases, embeddings, chunking, hybrid search, reranking, hallucination mitigation, long-context tradeoffs, RAG versus fine-tuning, retrieval evaluation, and production performance tuning.
Master RAG (Retrieval-Augmented Generation) technology: core principles, architecture design, and vector database applications. Includes complete Python code examples and RAG vs fine-tuning comparison.
Learn how vector databases power semantic search and RAG. Compare Pinecone, Milvus, Qdrant, Weaviate, and Chroma with HNSW concepts and code examples.
Deep dive into semantic search: differences from keyword search, embedding model selection, vector similarity calculation, hybrid search strategies. Includes Sentence-Transformers code examples and vector database implementation for building high-quality semantic search systems.
Deep dive into the retrieval bottlenecks of RAG systems. This article explores in detail how to significantly improve the accuracy of Top-K recall by introducing Hybrid Search and Rerank models, complete with architecture design and practical code.
Why do RAG systems still hallucinate? This article systematically summarizes 5 engineering methods to reduce RAG hallucinations, from data processing and retrieval strategies to Prompt engineering, drastically improving the accuracy of Knowledge Base QA.
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 knowledge graphs model entities, relations, and triples. Build with Neo4j, compare graph databases, and apply GraphRAG to RAG, search, and AI systems.
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.
Deep dive into AI search engine architecture including Perplexity, SearchGPT, and vertical AI search systems. Learn how RAG pipelines, query understanding, and answer synthesis power next-gen search. Includes architecture diagrams and implementation patterns.
A deep technical guide to Agentic RAG: how AI agents transform static retrieval pipelines into dynamic, self-correcting systems. Covers 4 design patterns (Routing, Multi-step, Corrective, Adaptive), architecture comparison with naive RAG, LangGraph implementation, and production best practices.