Build Reviewable AI-Assisted Engineering Work
Learn how to structure prompts, verify outputs, and preserve the records needed to support professional engineering decisions.
Artificial intelligence can accelerate research, drafting, analysis, code review, and project administration, but useful output depends on more than asking a good question. Engineers also need to control the sources provided, define assumptions and constraints, verify technical content, document material revisions, and preserve appropriate project records. This course presents a practical framework for moving from an initial prompt to a reviewed and defensible engineering work product.
Syllabus
Module 1 — Why Prompting and Recordkeeping Matter
Understand how prompts, inputs, outputs, revisions, and approvals affect the reliability and defensibility of AI-assisted engineering work.
Module 2 — Defining the Engineering Task
Learn to establish the objective, intended use, audience, constraints, assumptions, units, jurisdiction, and required level of review.
Module 3 — Building Strong Engineering Prompts
Use structured instructions, controlled sources, output requirements, and uncertainty handling to produce more reviewable results.
Module 4 — Controlling Sources and Project Context
Distinguish authoritative project information from background material and prevent AI tools from inventing missing facts or citations.
Module 5 — Staged Prompting and Workflow Design
Separate research, extraction, analysis, drafting, critique, verification, and approval into manageable quality-control steps.
Module 6 — Documenting Material AI Assistance
Determine when to preserve prompts, source sets, outputs, assumptions, revisions, verification, reviewers, and approvals.
Module 7 — Risk-Based Recordkeeping
Match the depth of documentation to the consequence, complexity, novelty, confidentiality, and reversibility of the task.
Module 8 — Confidentiality and Data Minimization
Protect client, employer, proprietary, personal, security-sensitive, and contract-restricted information when creating AI inputs and records.
Module 9 — Verification, Revision Control, and Approval
Connect AI-assisted work to authoritative sources, independent checks, version control, peer review, and final professional approval.
Module 10 — Implementing a Repeatable Engineering Process
Apply templates, checklists, naming conventions, approval gates, and organizational controls to routine AI-assisted workflows.