{"slug": "building-my-first-rag-system-deriving-the-architecture-from-first-principles-one", "title": "Building My First RAG System: Deriving the Architecture from First Principles - Part One", "summary": "A developer detailed the process of designing a Retrieval-Augmented Generation (RAG) system from first principles, aiming to create a unified knowledge base across scattered sources like Logseq, Gmail, and Notion. The post outlines the core components—retrieval, ingestion, storage, and generation—and emphasizes deriving architecture from the problem rather than starting with code. The developer's goal is to enable querying personal knowledge with natural language, similar to tools like NotebookLM but more integrated.", "body_md": "I recently read an [article](https://africa.businessinsider.com/news/im-a-vc-who-finds-dollar120-worth-of-ai-subscriptions-more-productive-than-an-intern/t0963k1) 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:\n\nEvery 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.\n\nLike 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.\n\nAs 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:\n\nTwo years ago, I read an [article](https://www.raptitude.com/2021/03/what-raptitude-has-always-been-about/) 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.\n\nWith 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.\n\nRetrieval-Augmented Generation is the process of supplementing LLM (Large Language Model) prompts with relevant information retrieved from your data. Instead of just relying on general training data, an LLM can answer your questions with reference to your actual information. The typical steps:\n\nThe core problem:\n\nFind the right knowledge at any given time from multiple, diverse sources and provide good enough context for AI to answer questions.\n\nTo solve this from first principles, what capabilities are required? At a minimum, the system must:\n\nThese lead straight to the major system components:\n\n**Retrieval:** Finds the knowledge base items most relevant to the user's question. This is the heart of RAG. It ranks results by relevance. E.g., if you ask about an ADHD blog post:\n\n```\n  Blog post whose title/content strongly matches the ADHD query - very relevant\n  Personal note discussing ADHD - relevant\n  A tweet about ADHD - probably relevant\n  Random tweet - probably irrelevant\n```\n\nFor now, ranking can be bundled into retrieval; in larger systems, it might be a separate stage.\n\n```\nflowchart TD\n    A[Knowledge Sources]\n    B[Source Connectors]\n    C[Ingestion]\n    D[Processing]\n    E[Knowledge Storage / Indexing]\n    F[Retrieval]\n    G[Context Preparation]\n    H[LLM Generation]\n    I[Answer with References]\n\n    A --> B\n    B --> C\n    C --> D\n    D --> E\n    E --> F\n    F --> G\n    G --> H\n    H --> I\n```\n\nWe’ve broken down the essential building blocks of a RAG system—without writing code yet. The aim was to clarify the problem so that the technical architecture emerges naturally.\n\nIf I want an AI system that can answer questions based on my dispersed personal knowledge, it must fetch that knowledge, ingest and process it into a standard form, store it accessibly, retrieve the most relevant parts, prepare them as LLM context, and use all this to generate useful responses.\n\nBefore now, I understood concepts like embeddings, vector databases, and indexing as isolated pieces. Taking a first-principles approach reframed it as a system design problem: how do I move knowledge from raw sources to usable LLM context?\n\nNow, things like embeddings and indexing become tools for certain subproblems—not the whole system.\n\nIn the next part, we’ll look deeper at how these components work together, what the actual data flow looks like, and where important design decisions come in.", "url": "https://wpnews.pro/news/building-my-first-rag-system-deriving-the-architecture-from-first-principles-one", "canonical_source": "https://dev.to/timilehin-olusegun/building-my-first-rag-system-deriving-the-architecture-from-first-principles-part-one-n43", "published_at": "2026-08-30 15:38:18+00:00", "updated_at": "2026-08-30 15:52:51.698325+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-tools", "developer-tools"], "entities": ["NotebookLM", "Logseq", "Gmail", "Notion", "Slack", "Twitter"], "alternates": {"html": "https://wpnews.pro/news/building-my-first-rag-system-deriving-the-architecture-from-first-principles-one", "markdown": "https://wpnews.pro/news/building-my-first-rag-system-deriving-the-architecture-from-first-principles-one.md", "text": "https://wpnews.pro/news/building-my-first-rag-system-deriving-the-architecture-from-first-principles-one.txt", "jsonld": "https://wpnews.pro/news/building-my-first-rag-system-deriving-the-architecture-from-first-principles-one.jsonld"}}