Generative AI / Intermediate: retrieval and controlled workflows

Generative AI β€” Intermediate: retrieval and controlled workflows

Connect authorized retrieval, RAG, evaluation, tools, agents, and privacy into explicit application boundaries.

6 lessons Β· No account required

What you will learn

  • Trace retrieval and answer-support failures independently.
  • Enforce tool scope and bounded control flow outside generation.
  • Protect sensitive data and evaluate untrusted content safely.

  1. Retrieval, chunking, and access scope

    Build and evaluate a source-selection boundary that preserves meaning, freshness, provenance, and authorization before generation.

    Start learning

  2. RAG, grounding, and citations

    Connect authorized retrieval to claim support and explicit abstention while recognizing that added context does not automatically make an answer true.

    Start learning

  3. Evaluation fixtures and error analysis

    Define representative tasks, labels, metrics, and regression decisions without turning a selected example or one average into a universal quality claim.

    Start learning

  4. Tool proposals and permission boundaries

    Treat model-selected actions as untrusted proposals that need deterministic schema, scope, approval, and effect ownership.

    Start learning

  5. Agents, control flow, and stopping

    Design bounded propose-check-act-observe loops with explicit state and evidence rather than treating an autonomous-looking transcript as reliable completion.

    Start learning

  6. Privacy and untrusted instructions

    Minimize exposed data and prevent source content from acquiring application authority across retrieval, generation, and tool boundaries.

    Start learning
Back to course