Graph RAG - The RAG technique that uses knowledge graphs to enable multi-hop reasoning across entity relationships. This article explores how Graph RAG solves relational queries that traditional vector search cannot handle, when to use it, and how to implement it with Neo4j and other graph databases. For a comprehensive comparison of RAG frameworks including Graph RAG, see this research analysis . Key Topics: The multi-hop reasoning problem in traditional RAG How knowledge

Context Contextual RAG - Anthropic's breakthrough technique that reduces retrieval failures by 67% through LLM-generated context augmentation. This article explores how Contextual RAG solves the ambiguous chunk problem, when to use it for high-stakes applications, and how to implement it for legal, medical, and financial use cases. For a comprehensive comparison of RAG frameworks including Contextual RAG, see this research analysis . Key Topics: The ambiguous chunk problem

Context Hybrid RAG - The production-standard RAG technique that combines keyword search ( BM25 ) with vector similarity search . This article explores why Hybrid RAG has become the de facto standard for enterprise deployments, how it works, and when it delivers the best results. For a comprehensive comparison of RAG frameworks including Hybrid RAG, see this research analysis . Key Topics: Why Hybrid RAG: combining keywords and semantics BM25 keyword search fundamentals Recip

Context Naive RAG - The foundational RAG technique that combines vector similarity search with LLM generation. This article explores how Naive RAG works, when to use it, real-world applications, and why it remains the starting point for most RAG implementations despite its limitations. For a comprehensive comparison of RAG frameworks including Naive RAG, see this research analysis . Key Topics: Vector similarity search fundamentals Embedding models and vector databases Retr

Context What Is RAG? - A comprehensive introduction to Retrieval-Augmented Generation (RAG) for technical practitioners new to the concept. This article explains why RAG exists, how it solves fundamental LLM limitations, and why it's become essential for production AI applications. Key Topics: The fundamental problem: LLM knowledge limitations What RAG is and how it works Why RAG matters: real-world impact RAG vs. fine-tuning: when to use each The RAG landscape: from simple