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Measure a real AI workload end to end, and record every number so it can be checked.
A hands-on measurement lab covering SCI and SCI for AI, four measurement approaches: research estimates, provider data, your own or third-party estimation models, and direct measurement, plus intensity, embodied impact, boundaries and reporting.
11h 10m
14
30 question
80%
Certification
SCI for AI Practitioner: The Measurement Lab
How the course works
A lab, not a lecture
You measure a real workload end to end, then compare the approaches against each other.
Evidence recorded as you go
Method, level, boundary and date on every number, which is what makes a figure defensible later.
Built on the ratified specification
SCI (ISO/IEC 21031) and SCI for AI, cited precisely, with pathway status stated.
What this course helps with
Answer the second question
"What does it include?" is where most footprint figures come apart. The boundary discipline is the core of the course.
Interpret provider data
What a vendor figure covers, what it omits, and how to use it without overstating what it says.
Explain why tools disagree
Four approaches, different results, and a way to reconcile and explain the gap. The energy and carbon figure you publish is the one that holds when a reduction is claimed against it.
Who this is for
- Engineers, architects and sustainability practitioners producing AI footprint figures
- Practitioners preparing figures for disclosure
- Anyone answering customer, audit or regulatory questions about AI impact
Related courses:
To govern measurements rather than produce them, Green AI for Executives covers the leadership view.
What you will work with
One score, four inputs
The SCI structure applied to AI, with the functional unit treated as the decision it is.
The reconciliation
Why four approaches disagree, and how to explain the difference.
Boundaries and the register
What is in, what is out, and where it is recorded for the next person.
What you will be able to do
Course Lessons
The full course
01
The Prompt Footprint
Introduces the five-dimensional view: cost, carbon, energy, water, embodied hardware. Establishes SCI for AI as the standards anchor. Positions the regulatory landscape (CSRD, EU AI Act, procurement disclosure) as the why now for Footprint literacy.
Before You Begin
Modules
02
The Specification Lever
How prompt intent quality drives Footprint. The GSF Intent Framework introduced. Trade-offs: over-specification wastes tokens, under-specification wastes retries. Worked examples across capability tiers.
Modules
03
The Structure Lever
Prompt organization: system messages, few-shot examples, output constraints, formatting instructions. Where structure earns its tokens and where it doesn't.
Modules
04
The Reasoning Depth Lever
When to invoke chain-of-thought, tree-of-thought, extended reasoning modes; when not to. The GSF Reasoning Budget: task-class taxonomy for reasoning depth decisions.
Modules
05
The Model Selection Lever
The most consequential single decision in inference efficiency. Capability tiers and routing decisions. The GSF Model Fit Test protocol. Same task across tiers, measured.
Halfway Check-in
Modules
06
The Orchestration Lever
Where agents leak resource: tool selection, chain depth, redundant retrieval, missing caching. Token budgets as a first-class design constraint. Case study from the supply-chain agent paper (70–90% workflow-level savings).
Modules
07
Measuring the Prompt Footprint
Hands-on demonstrations with EcoLogits, CodeCarbon, AI Energy Score. Producing numbers a CSRD auditor can read. The measurement chain from token count to gCO₂eq.
Modules
08
Procurement & Disclosure
What to ask your LLM vendor. What CSRD and the EU AI Act will require. The GSF Procurement question bank organized by Footprint dimension.
Modules
09
Course Completion
50 questions, 120 minutes. Pass Marks: 85%
Final Assessment
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Assessment & Certification
Time to complete