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Technical Deep-Dive

Comprehensive technical understanding of RAG architecture, memory patterns, and production AI systems for practitioners building agentic systems. This path covers retrieval augmentation, context compilation, and deployment patterns in depth. You will learn how to build knowledge graphs, implement multi-agent coordination, and deploy these systems in production. Ideal for engineers and architects who want hands-on understanding of the technical foundations.

4-5 hours
7 stops
Knowledge Graph ArchitectureAgentic Context Engineering

Reading Path

  1. 1

    Building the Knowledge Graph

    My first knowledge graph was a disaster. I had read about 'second brain' systems, watched videos about linked notes, and downloaded Obsidian with grand ambitions. Six weeks later, I had 847 notes, no discernible structure, and a search that returned garbage regardless of what I queried.

    “Establishes the foundational retrieval architecture before diving into agent orchestration”

  2. 2

    The Agentic Mesh

    AI agents aren't independent intelligences. They're orchestrated loops of prediction and action, built on language models that generate probable text given their context.

    “Introduces the four-layer memory architecture that agents use to manage context”

  3. 3

    Cluster A: Context Engineering Deep-Dive (A1-A3)

    “Technical implementation details for memory tiering and context compilation”

  4. 4

    Cluster A: Multi-Agent Patterns (A4-A6)

    “Coordination protocols and message-passing patterns between specialized agents”

  5. 5

    Cluster B: Semantic Search & Embeddings (B1-B3)

    “Vector database configuration and embedding strategy implementation”

  6. 6

    Cluster B: Hybrid Retrieval (B4-B6)

    “Combining graph traversal with vector similarity for optimal recall”

  7. 7

    From Theory to Production

    The gap between understanding infrastructure thinking and actually having infrastructure is where most people get stuck.

    “Synthesis chapter connecting all technical components into deployable systems”

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