[ Masterclass Roadmap ]

The AI Systems Architect Roadmap

A 15-part masterclass on building production-grade AI systems — from rethinking the MERN stack to hybrid retrieval, observability, FinOps, and stateful agents.

15 lessons•15 published•~98 min total
  1. 01
    FoundationsPublished

    The AI-Native Dev Stack

    MERN didn't die — it grew a nervous system. The three new tiers every stack now needs.

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  2. 02
    FoundationsPublished

    Beyond the Prompt

    Tokens, the context window as a byte budget, and why determinism is a parameter — not a property.

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  3. 03
    FoundationsPublished

    Vector Foundations

    Turning meaning into geometry so 'reset my password' finds 'recover account access.'

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  4. 04
    SystemsPublished

    RAG: Grounding the Agent

    The grounding contract that turns a confident stranger into an expert who cites the manual.

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  5. 05
    SystemsPublished

    Agentic Control Loops

    A chatbot answers; an agent acts. The observe-decide-act loop that makes autonomy tractable.

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  6. 06
    ProductionPublished

    Latency-First AI

    Users forgive a wrong answer faster than a slow one. Streaming first tokens from the edge.

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  7. 07
    ProductionPublished

    The Model Context Protocol

    The USB-C of AI tooling — collapsing N×M bespoke integrations into N+M servers and clients.

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  8. 08
    ProductionPublished

    94% Compression

    How we cut an agent-graph payload by 94% — by designing the format around the data, not gzip.

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  9. 09
    ProductionPublished

    Guardrail Engineering

    Deterministic checks wrapped around a probabilistic core. Defense-in-depth that fails closed.

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  10. 10
    ProductionPublished

    Hybrid RAG

    Dense vectors miss exact codes; keywords miss paraphrase. Fuse BM25 and vectors, then rerank.

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  11. 11
    ProductionPublished

    LLM Observability

    A multi-step agent that only logs is a black box. One OpenTelemetry span per step makes it readable.

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  12. 12
    ProductionPublished

    FinOps for AI

    Cost per request is a property you design — cache so you never pay twice, route so you never overpay.

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  13. 13
    ProductionPublished

    Evaluation-Driven Dev

    A prompt is code with no tests until a golden dataset gates the merge on a regression threshold.

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  14. 14
    ProductionPublished

    Memory & Stateful AI

    The context window is RAM, not disk. Tier memory into a working buffer, episodic summaries, and durable facts.

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  15. 15
    CareerPublished

    The AI-Native Portfolio

    Show systems, not tool usage. Turn the portfolio itself into proof-of-work for lead roles.

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