RAG & Retrieval

Hands-on experiments in retrieval-augmented generation: chunking strategies, embeddings and their limits, query expansion, and why retrieval quality decides what your LLM can do.

12 articles

• Geek Out Time: Why RAG Alone Fails Enterprise Governance (And How Ontology Fixes It)Dec 2025
• Geek Out Time: DeepSeek-Powered Query Expansion and Self-Querying in Advanced RAGOct 2025
• Geek Out Time: When Geometry Fights Back — Why Your Embeddings in RAG Can’t Think in ‘AND’Oct 2025
• Geek Out Time: The “Rich” Get Smarter in RAG-Continuing from “Embeddings”Sep 2025
• 𝗚𝗲𝗲𝗸 𝗢𝘂𝘁 𝗧𝗶𝗺𝗲: Embeddings -Continuing from “Chunk Happens” (RAG)Sep 2025
• 𝗚𝗲𝗲𝗸 𝗢𝘂𝘁 𝗧𝗶𝗺𝗲: Chunk Happens — Testing different Chunking Strategies for RAGJul 2025
• Geek Out Time: Routing Meets Retrieval-Context-Aware Multi-Agent Orchestration with AG2 + FAISSJul 2025
• Geek Out Time: Simulating LLM Short and Long Memory with FAISS, LangChain, and Google ColabJun 2025
• Geek-Out Time: RAG vs. CAG in Google ColabMar 2025
• Geek Out Time: Exploring Opensource AnythingLLM — The All-in-One, Easy AI Platform for Local RAG…Dec 2024
• Geek out time: try LLM and Embeddings on Nvidia NIM with Node.jsJul 2024
• Geek Out Time: Play with LangChain 3 — Simulate Full RAG Locally with word2vec and GemmaApr 2024