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Standard vector RAG is great for finding needles, but it’s terrible at describing the haystack. It relies entirely on semantic proximity. That means it often fails at multi-hop reasoning or answering
I've been spending a lot of time lately building autonomous agents with ReAct and RAG frameworks. The shift from stateless chatbots to stateful systems that can actually execute tools is incredibly us
I love the idea of autonomous coding agents. But one of the quickest ways to hit a wall when setting them up is the infinite context problem. Your context window is finite, but the work you want the a
Building LLM apps is easy, but scaling them without setting a pile of money on fire is hard. You really don't need the massive brainpower of GPT-5 for every single user query. Routing is how we fix th
Building a multi-turn conversational AI is surprisingly easy right now. Evaluating it is incredibly hard. For single-turn tasks, a standard static dataset works fine: you just feed in a prompt and ass
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 simp
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 bottle
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 i
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 probabl