Blogs & Notes
The Complete Ontology Guide: What, Why, and How to Build and Use One with LLM Context Retrieval
What an ontology actually is, what goes inside it and in which format, how it relates to a knowledge graph, where it pays off, how LLMs build one now, and how it gets used to answer real questions, ground agents, and keep data queries honest.
RAG Without a Vector Database: How PageIndex Works Context Retrieval
RAPTOR and GraphRAG added structure but kept the vector core. PageIndex deletes it — no embeddings, no chunking, no vector DB. Instead it turns a document into its own table of contents and lets an LLM reason about where to look.
GraphRAG vs RAPTOR: Advanced RAG Explained with Examples Context Retrieval
Plain vector RAG chops everything into flat chunks and loses the big picture. GraphRAG and RAPTOR add structure back — a visual, step-by-step guide to how each one works, how they differ from normal RAG, and when to reach for them.
LLM Wiki Explained: Karpathy's Knowledge Base vs Traditional RAG Context Retrieval
Karpathy's LLM wiki idea in plain terms: a knowledge base the model compiles and maintains for you, why it beats retrieve-and-forget RAG, how to build one step by step, and whether it survives the enterprise.
How to Improve RAG Retrieval Accuracy: 8 Techniques That Work Context Retrieval
A practical, visual field guide to the techniques that actually lift retrieval accuracy in RAG: fixing the query, the search, the results, and knowing when to just let an agent drive.
RAG Chunking Strategies: 7 Techniques for Better Retrieval (2026) Context Retrieval
A practical and visual field guide to the chunking techniques that actually work in production in 2026: what to cut, and how to give each chunk the context it needs.
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