I’m Andrew Rogerson, based in Berlin. I work across product, design, and engineering to turn complex ideas into useful products and scalable systems—from discovery and architecture through to production.
My current focus is AI-enabled products: agentic workflows, RAG systems, evaluation, and the engineering foundations that make them reliable in production.
LangChain is a framework for connecting language models to prompts, tools, data, and application logic. Here are the core concepts — prompt templates, chains, retrieval, memory, and agents — and when you actually need them.
Generative AI creates content in response to a prompt. Agentic AI pursues a goal through a series of actions. Here is how the two differ, how they share a common foundation, and why the future likely combines both.
LangChain and LangGraph both help you build LLM-powered applications, but they solve different problems. Here is how they differ, when to reach for each, and why they work well together.
Rushing produces mistakes, rework, and delays. A better approach: begin deliberately, build a reliable process, and let speed emerge from consistency.
