What is RAG for AI wikis?

RAG retrieves relevant docs from your bloated wiki via embeddings, feeds only those to the LLM — skips cramming everything.

How do I add RAG to Obsidian?

Grab ObsidianRAG: Ollama + ChromaDB setup indexes your vault, queries smart with hybrid search and reranking.

Yes, tools like ObsidianRAG run fully local with Ollama models — no API calls, total privacy.

🤖 AI & Machine Learning

Andrej Karpathy's LLM wiki dream hits a wall at scale. RAG — Retrieval Augmented Generation — is the pragmatic fix, if you skip the hype.

theAIcatchup Apr 08, 2026 3 min read

RAG scales AI wikis past context limits by retrieving only relevant chunks, slashing tokens 100x. 𝕏
Skip naive vector search; hybrid + rerank + graph expansion in tools like ObsidianRAG delivers precision. 𝕏
Open-source tools dominate — no profits, just pragmatic fixes for dev hoarding. 𝕏

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Community-driven. Code-first.

#AI Wiki #Karpathy Wiki #Obsidian RAG #ObsidianRAG #RAG #Retrieval-Augmented Generation #vector embeddings

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Originally reported by Dev.to