No, Small Models Are Not the "Budget Option"

Small and local LLMs are often framed as the cheap alternative to frontier models. That framing is wrong. They are not a degraded version of the same thing. They are a different architectural choice, selected for control, predictability, and survivable failure…

GraphRAG Part 2: Minimum Viable GraphRAG

In Part 1, we explored why GraphRAG matters. Now let's build a minimum viable GraphRAG with three extraction modes: Mode LLM Calls Best For Heuristic (default) 0 per chunk Fast indexing, structured markdown Hybrid 1 per document Balance of speed and quality…

GraphRAG: Why Vector Search Breaks Down at the Corpus Level

Your RAG system is great at "needle" questions: retrieve a few relevant chunks and synthesise an answer. It struggles with two common query types: Sensemaking: "What are the main themes across this corpus?" Connective: "How does X relate to Y across different…

Probability Is Not a System: The Ten Commandments of LLM Use

I keep seeing the same failure mode in "AI-powered" systems: LLMs are being asked to do jobs we already solved decades ago - badly, probabilistically, and without guarantees. This isn't cutting edge. It's a regression. And this isn't a beginner mistake. The…

DataSummarizer: Fast Local Data Profiling

Most “chat with your data” systems make the same mistake: they treat an LLM as if it were a database. They shove rows into context, embed chunks, or pick “representative samples” and hope the model can infer structure, quality, and truth from anecdotes. It…