What you'll learn
⚡ Write engineering-grade prompts that get usable, correctly-structured controls output first time
⚡ Give Claude data properly, iterate on weak answers, and build a reusable prompt library
⚡ Draft and stress-test scope, WBS and estimates — and produce a defensible basis-of-estimate
⚡ Use advanced AI methods including chain prompting, role stacking, and iterative refinement
⚡ Turn scope into activities, interrogate schedule logic and float, and write clear schedule narratives
⚡ Model budgets, cash flow and S-curves; build risk registers; and frame qualitative and quantitative risk
⚡ Run full Earned Value analysis (CV, SV, CPI, SPI), forecast EAC/ETC/TCPI, and write honest dashboard commentary
⚡ Produce faster status reports, executive summaries, emails, minutes, and contract/change correspondence
⚡ Review and interrogate long documents and contracts, and capture lessons-learned at close-out
⚡ Run an end-to-end controls cycle on a real project with Claude as your assistant — scope through close-out

Requirements
❗ No AI or prompt engineering experience required
❗ Basic understanding of project management concepts is helpful but not mandatory

Description
“This course contains the use of artificial intelligence.”

Claude AI in Project Planning Scheduling & Cost Controlling
You already know project controls. This course shows you how to do it faster and sharper, with Claude AI working alongside you — drafting, structuring, calculating, explaining and checking — while professional judgement and accountability stay firmly in your hands.

Most "AI for professionals" courses stop at generic tips. This one is built specifically for the controls workflow. Across40 practical lessons in 9 modules, you'll move through the entire cycle on a realistic infrastructure project: writing engineering-grade prompts; building and stress-testing scope, WBS and estimates; producing schedules and clear schedule narratives; modelling cost, cash flow and risk; running Earned Value analysis and defensible forecasts; and generating status reports, executive summaries, contract and change correspondence, and lessons-learned — then automating the repetitive parts and governing AI use across a team.

Every lesson follows the same rhythm: the underlying controls principle first, then exactly how to get Claude to do it well, then how to verify the output before you rely on it. You'll see the prompts, the expected results, and the traps — so you're never copying blindly.