A RAG agent does a lot of work before it answers a question. It searches documents, decides which passages matter, chooses whether to search again, and checks whether its answer matches the sources. Each step can involve a small judgment call. I used to rely on language models for many of those calls. They worked, … Read more
Google recently shipped a new open spec called the Open Knowledge Format, or OKF. Their core argument is simple and, frankly, hard to disagree with. Large language models are no longer held back mainly by raw intelligence. They’re held back by context. If a model doesn’t understand your app, your business, your systems, or your … Read more
Baidu recently released an open weights vision language model called Unlimited-OCR, and the name tells you the pitch straight away. The idea is simple. Feed it a long document and let it transcribe the whole thing in one pass, page after page, without the usual slowdown that hits most models as output grows. That grabbed … Read more
When an AI agent goes looking for information and comes back empty-handed, the usual assumption is that the answer was missing. A lot of the time, that is not the real problem. The answer may have been sitting right there in the source material. It just didn’t survive the trip into a machine-readable format. That … Read more
There’s a huge race happening in AI right now to define what comes after conventional RAG. By this point, most people have heard some variation of “RAG is dead.” I don’t buy that framing. It’s usually too dramatic, and the proposed replacements are often too simple. What is true is that basic retrieval pipelines break … Read more
Over the last year, I’ve seen a steady stream of headlines declaring that RAG is dead. Then the proposed replacement changes every few weeks. Sometimes it’s long context windows. Sometimes it’s agentic file exploration. Sometimes it’s context engineering, memory systems, or agent skills. Most of those headlines are too dramatic. But something genuinely interesting did … Read more
I created an automation that treats a knowledge base more like a living file system than a static set of vectors. Instead of relying only on semantic search, this approach lets an AI agent actually explore folders and files, form hypotheses about where answers live, and then dig deeper—just like a developer reading a codebase. … Read more
Overview I built a full-featured Agentic RAG application that lets an LLM interact with private company data safely, efficiently, and transparently. The stack is intentionally simple: a React frontend, a Python FastAPI backend, and Supabase for storage, vectors, and auth. I used DocLing for document parsing and LangSmith for observability. The result is a multi-user … Read more
I built an automation that lets AI agents answer questions about structured data without hallucinating. It uses natural language query, not just vector stores. The result is a database agent that learns useful SQL patterns over time and reuses them when similar questions come up again. Why vector stores alone often fail for tabular data … Read more
I built AI agents for businesses and quickly learned that vector search is powerful but not a cure-all. Vector search shines for conceptual, fuzzy queries. It struggles when answers require exact matches, structured calculations, or chaining facts across systems. Over hundreds of projects I helped with, the same blind spots showed up again and again. … Read more
I built an automation that fixes the single biggest weakness in most retrieval-augmented generation systems. The problem is simple: agents retrieve isolated fragments of documents, but they have no idea where those fragments sit in the document structure. That missing structure strips away the meaning those fragments need. I call the solution context expansion. It … Read more
Why agents need a map of your data I build AI agents all the time. They are great at language and reasoning. What they often lack is context about how pieces of data relate to each other. An agent can answer questions about a single record. It struggles when the answer depends on connections across … Read more
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