Deep explorations of agentic AI, RAG architectures, and production engineering.
64 articles
A practical, opinionated comparison of the four vector databases people actually argue about, with a clear default and the signals that mean you have outgrown it.
Embeddings turn text into vectors so meaning becomes geometry. Here is the mental model, the gotchas, and how to choose a model in 2026.
Rerankers re-score your top candidates with a model that actually reads the query and the document together. Here is how they work and when they pay off.
Chunking quietly decides how good your RAG system can ever be. Here is how the main strategies compare and a simple way to pick one.
Deep Agents bakes planning, context management, subagents, and human approval into the agent loop. Here is why it exists, every main feature, and a production-grade customer support agent built with it.
You cannot improve an agent you cannot measure. A deep, practical guide to evaluating agents with LangSmith: tracing, datasets, evaluators, experiments, and what makes agent evaluation different.
A hands-on guide to building MCP servers with FastMCP, drawn from two I actually shipped: the anatomy, the rules, stdio vs remote HTTP, the pitfalls, and how to test and deploy.
An agent with no tools is just a chatbot. Here is how tool calling really works, what makes a tool the model can use reliably, and how MCP lets you plug in tools you did not write.
Traditional RAG retrieves once and hopes for the best. Agentic RAG lets the model decide whether, what, and how to retrieve, then check its own work. Here is the difference, with code.