Hey all, I wrote a new article about a technique that has come up over and over in my work, especially in my Cheat at Search training, for doing effective query understanding into a large vocabulary. Instead of asking an LLM to classify into a vocabulary. Ask it to hallucinate fake entities, then resolve those to real ones client side. Save yourself a lot of tokens and use cheaper models. https://softwaredoug.com/blog/2026/08/10/hypothetical-classifications -Doug PS - A reminder that Vectors...

Almost all my clients face this issue. They want to build a RAG system. They have: Millions of PDFs That turn into Billions of chunks + embeddings Turns into become petabytes of floats That's the recipe of nearly every speculative RAG project I've worked on. A mature search team stands up a vector database and can't tame the sheer scale of data they're facing. That drags project timelines out months while costs skyrocket. It's important to have tools to manage the scale. One often overlooked...

An agent with grep does quite well at search. It’s shocking to search technologists. We want to cocoon the problem in technology. We care about algorithms, knowledge graphs, ranking: ever-and-ever smarter retrieval. It turns out, though, you can play a bit of a trick. If you convince everyone to optimize content for your search engine, you’ll have built the best search engine. There’s Sutton’s bitter lesson about unleashing raw compute on a problem. But there’s a different bitter lesson in...