Building My First RAG System: Deriving the Architecture from First Principles - Part One

Intro I recently read an article about a VC who uses AI to boost his productivity. He described building a knowledge base using NotebookLM, and one point that stuck with me was: Every time I read something online that I thought I wanted to remember, I'd copy and paste it into that repository. Whenever I wanted to write a blog post, I could query it and retrieve all the information I needed. Like him, I have knowledge and resources scattered across Logseq, Gmail, Notion, ADR documents, Slack, project readmes, Markdown files, Twitter, and more. That made me wonder: how could I build my own system? Tools like NotebookLM exist, but I want a single knowledge layer across all my sources—not isolated, manually managed workspaces. NotebookLM’s model requires creating a workspace, adding sources, and asking questions about them, but separate notebooks mean separate contexts. As an experienced engineer who’s never built a Retrieval-Augmented Generation (RAG) system, I saw this as an opportunity to learn and share. I’ll approach it from first principles, and in this series, we’ll: Architect a RAG system from the ground up. Break its subsystems down and clarify their responsibilities. Identify architectural decisions and tradeoffs. Integrate the RAG system with an LLM to create something like a personal Google Search for your whole digital life. Use Case Two years ago, I read an article about a man with ADHD. The post stayed with me, but for over a year I couldn’t find it again, even after searching bookmarks and Googling "article about a guy with ADHD". I finally found it because the author emailed it to his mailing list. Without that email, I might never have seen it again. With a personal knowledge base (RAG system), I could have simply asked for "an article about a guy with ADHD" and quickly found it. Let’s dive into how such a system works. What is Retrieval-Augmented Generation (RAG)? Retrieval-Augmented Generation is the process of supplementing LLM (Large Language Model