· technical  · 3 min read

RAG, But Make It Real Life: How I Use Retrieval to Run My Brain and Business

DRAFT

Outline

Hook: RAG (Retrieval-Augmented Generation) sounds like a machine learning paper topic, but it’s actually how your brain already works: you don’t remember everything—you remember where to find it, then reconstruct the answer on demand. I’ve just systematized it with AI.

Core Argument: The most powerful personal AI systems aren’t trained models—they’re retrieval systems over your own knowledge. By combining smart capture, semantic search, and LLMs that can synthesize your own past thinking, you create a “second brain” that actually works.

Key Sections:

  1. What RAG Actually Is (Without the Jargon)

    • Simple definition: Fetch relevant context, feed it to AI, get informed answer
    • Why it’s better than pure LLMs for personal use
    • The three components: storage, retrieval, generation
    • Real-world analogy: Your brain + Google + an AI that reads for you

  2. My Personal RAG Stack

    • Storage layer: Obsidian for notes, 99 Minds for ideas, Supabase for structured data
    • Retrieval layer: Vector embeddings (OpenAI), semantic search, keyword hybrid
    • Generation layer: GPT-4 with custom system prompts per context
    • Interface layer: Custom Streamlit dashboard, mobile shortcuts, voice queries
    • Why I chose these tools (and what I’d change)

  3. Use Case #1: Personal Knowledge Management

    • Every book note, article highlight, personal reflection in one searchable place
    • Query: “What did I learn about X?” → Retrieves and synthesizes across months
    • The power of connecting dots you forgot existed
    • Example: Combining insights from 3 books I read years apart

  4. Use Case #2: Business Context Repository

    • Client conversations, project decisions, technical patterns
    • Query: “How did we solve authentication for the law firm project?”
    • Institutional memory for a team of one (or five)
    • Reduces “reinventing the wheel” dramatically

  5. Use Case #3: Creative Synthesis

    • Music lyrics, story ideas, visual concepts
    • Query: “Show me all ideas related to ‘time’ theme”
    • AI helps find patterns across creative work
    • Not generating content—revealing connections

  6. The Technical Details (For Builders)

    • Chunking strategy: 500-token chunks with 50-token overlap
    • Embedding model: text-embedding-3-large
    • Vector database: Supabase pgvector
    • Reranking for precision
    • Cost optimization: caching, batching, local embeddings for dev

  7. Pitfalls and Solutions

    • Problem: Garbage in, garbage out → Be intentional about what you capture
    • Problem: Retrieval misses context → Hybrid search (semantic + keyword)
    • Problem: AI hallucinates despite context → Citation links, confidence scores
    • Problem: Privacy concerns → Local-first options, encrypted storage

Examples/Stories:

  • Concrete example: “How should I price this project?” query pulling from 5 past pricing discussions
  • Creative example: Finding thematic connections across 50 song ideas
  • Failure story: Early version retrieved irrelevant chunks, AI went off track
  • Success metric: Reduced “where did I write that?” searches by 90%

Takeaways:

  • RAG makes LLMs 10x more useful for personal knowledge work
  • Start simple: Obsidian + custom GPT with file uploads
  • The real work is capture discipline, not fancy AI
  • Your past self’s thinking is your most valuable training data
  • This is how “AI assistants” should work—grounded in your reality

Cross-Links:

  • ← “What I Learned Building 99 Minds” (Series 1-3)
  • → “Stop Asking ‘What Can AI Do?‘” (Series 1-5)
  • → “Why Your AI Agent Sucks at Context” (Series 1-8)
  • → “From Overwhelm to Pipeline” (Series 3-23)
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