Build the workflow you will actually keep.
This is where Foundations becomes operational. Pick one loop, define the inputs and checks, and leave with a system you can run again tomorrow.
Your first durable AI workflow should be small and repeatable.
Do not finish Foundations by building something enormous. Finish by defining one loop you can reuse weekly with low friction. Good examples: weekly brief creation, meeting prep, draft generation, pipeline summaries, research digestion, or follow-up writing.
The workflow design checklist
- Trigger: what starts the loop?
- Context: what material does the system need?
- Prompt asset: what reusable instruction set works best?
- Review: what must a human check?
- Output: where does the result live when finished?
Design for reuse, not novelty.
The workflow is good if it becomes easier to run over time. Save the prompt. Save the output shape. Save the checklist. Note the common failure modes. The point is not to impress yourself once; the point is to reduce drag every time the work comes back.
If you are ready for more depth after this, move into AI agent evaluation guide to understand production systems or go straight into AI agent evaluation guide to use a trust-bound workspace in practice.
Capstone exercise
Write your workflow on one page. Include:
- the weekly trigger,
- the input sources,
- your reusable prompt structure,
- the review rule, and
- the destination for the finished output.
If another person could run it with minimal explanation, you built something real.
Worked example: Write a repeatable weekly brief
Trigger: Friday review. Input: approved project notes. Prompt: summarize completed work, blockers and decisions. Review: check every name and date against the notes. Output: an internal draft approved by the project owner. Failure path: leave missing facts blank and ask the owner.
Check your exercise
Have another person follow the one-page workflow using fictional notes. They should know what to supply, what to check, when to stop and where the result goes. Record one improvement before using real data.
Illustrative example. Use fictional or non-sensitive inputs for practice.
Further reading: NIST AI Risk Management Framework for risk identification and evaluation.
Published by ALCUB3 · Updated October 24, 2026.