MAMundher Al-Shabi, PhDinmundher.com00Why Grep Won't Save Your RAG Pipeline5d ago · 3 min read · I’ve been reading through a recent paper titled "Is Grep All You Need? How Agent Harnesses Reshape Agentic Search". It’s a provocative piece with a premise I normally love. The authors claim that simpJoin discussion
MAMundher Al-Shabi, PhDinmundher.com10Harnessing Conversational AIMay 24 · 3 min read · I’ve been spending the last few weeks messing around with open-weight models to build conversational interfaces. By now, the new reality is obvious: generating natural language is no longer the bottleJoin discussion
MAMundher Al-Shabi, PhDinmundher.com00Vectorless RAGMay 17 · 5 min read · If you’ve built anything with LLMs in the past couple of years, you’ve probably wired up a Retrieval-Augmented Generation (RAG) pipeline. The playbook is burned into our brains: take a PDF, smash it iJoin discussion
MAMundher Al-Shabi, PhDinmundher.com00Thoughts on Advanced Chunking Strategies for RAGMay 4 · 3 min read · I’ve been thinking a lot recently about the "chunking problem" in Retrieval-Augmented Generation. If you've played around with the llm CLI tool or built anything with vector embeddings, you've probablJoin discussion
MAMundher Al-Shabi, PhDinmundher.com00All You Need is a Good ChunkingMay 3 · 4 min read · If you’ve spent any time building Retrieval-Augmented Generation (RAG) prototypes, you inevitably hit the exact same wall. You wire up a great embedding model, point it at an excellent local LLM, and Join discussion