Generative AI & LLM Engineering Foundations

Observe Without Exposing

Use telemetry and rollout gates to detect failures while limiting unnecessary collection of user content.

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The curriculum

  1. From Tokens to Context — Free preview

    Use a tiny attention calculation to distinguish model representations from understanding, memory, and truth.

  2. Datasets That Can Disagree With You — Free preview

    Split related examples together, define loss targets deliberately, and prevent evaluation leakage.

  3. Adapt With a Measured Budget — Sign-in access

    Understand low-rank updates and quantization costs before choosing optional hardware-heavy experiments.

  4. Make Baselines Hard to Beat — Sign-in access

    Compare candidates using category-level errors, abstentions, and a frozen evaluation protocol.

  5. Proposals Before Actions — Free preview

    Keep model-generated suggestions separate from authorized tool execution and durable workflow state.

  6. Latency Has Components — Sign-in access

    Measure request stages and design caches that respect versions, identity, and changing access rules.

  7. Observe Without Exposing — Sign-in access

    Use telemetry and rollout gates to detect failures while limiting unnecessary collection of user content.

  8. Release Signal Bench — Sign-in access

    Integrate the offline classifier, review workflow, measurements, and compatible rollback into a demonstrable capstone.