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Operations, product, and technical professionals

Build Reliable AI Workflows

Move from one-off prompting to a human-reviewed workflow with clear inputs, controls, and evidence of quality.

PT34M · Intermediate

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What you will learn

  • Break a task into steps suitable for AI assistance and human judgment.
  • Define quality checks, escalation paths, and approval boundaries.
  • Measure whether the workflow saves time without increasing risk.

Course outline

01

Map the work

Identify inputs, decisions, outputs, and the parts that require human judgment.

02

Add controls

Build validation, exception handling, and review into the workflow.

03

Pilot and improve

Test on representative cases and measure quality before scaling.

OPEN LESSON

Open lesson: Map the work

Before automating a task, define the result you need and the evidence that would make it trustworthy. In this course, the goal is: Design one repeatable AI-assisted workflow with checkpoints, failure handling, and human approval. AI can accelerate a draft, comparison, or exploration, but it does not know your policies, full context, or the cost of being wrong. Begin with a low-risk case and keep the earlier version so you can compare, explain, and reverse the decision.

  1. Define the deliverable and an observable quality criterion: Break a task into steps suitable for AI assistance and human judgment.
  2. Set context, allowed data, and human review before generating: Define quality checks, escalation paths, and approval boundaries.
  3. Check the output and document the final decision: Measure whether the workflow saves time without increasing risk.
Responsible authorVidAcademia Editorial Team
ReviewVidAcademia Learning Quality
MethodOutcome-first design, primary-source review, guided practice, and a human-verified final decision.

SOURCES AND EVIDENCE

Learn with references you can verify.

We select official sources and explain what each supports.

NIST · 2023

NIST AI RMF Playbook

The playbook provides suggested actions and documentation practices for Govern, Map, Measure, and Manage outcomes.

Content reviewed:

GO DEEPER

Open guides for practice before and after the course.

Use these guides to prepare the task, verify claims, and apply the outcome with human review.

APPLIED GUIDE

How to measure AI training ROI

Measure whether AI training changes a real workflow, improves quality, and saves time without adding unacceptable risk.

APPLIED GUIDE

Human review in AI workflows

Place qualified review where it can catch consequential errors instead of adding a ceremonial approval step.

APPLIED GUIDE

How to evaluate AI output quality

Create a small evaluation set with clear criteria, representative cases, edge cases, and documented reviewer decisions.