#rag
5 items tagged rag.
Three architectures of agent memory — and why Trail picked Compile
Karpathy, Tan, and Liu all start from the same diagnosis — your agent is a retriever, not a thinker. They reach three different architectures: retrieve (RAG), compile (LLM Wiki), and act (Fat Skills / GBrain). Trail picked Compile, deliberately. This is what we accept and what we believe is converging.
Compile-Time Knowledge: A Technical Argument Against RAG
RAG is a search engine wearing a language model as a hat. Trail compiles knowledge the way a brain consolidates memory — and the architectural difference shows up in latency, accuracy, provenance, and cost.
How a Brain Actually Remembers: Why Trail Compiles Knowledge Instead of Searching For It
RAG treats knowledge like a filing cabinet. Brains do something fundamentally different — and so does trail. The architecture matters more than the model.
Why Your Knowledge Base Should Compile, Not Search
Most AI knowledge systems are sophisticated search engines that forget between queries. The organizations winning are building something different — and the architectural choice has direct consequences for cost, accuracy, and defensibility.
Trail and NotebookLM: Same Ethos, Different Artifact
NotebookLM proved source-grounded ingest-time tools can ship to millions. Trail shares that ethos and diverges on one question: what the system leaves behind.