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Read a software water number, know what it covers, and know when it cannot be compared.
Six short modules in plain language: why water reaches every software role, the four layers of software water, consumption versus withdrawal, how to read a water number and state what it leaves out, why a litre in one place is not a litre in another, and the small things each role can do. No prerequisites and no calculation required.
2h 50m
6
12 question
80%
Certification
Software Water Essentials
How the course works
Basic on purpose
Plain language throughout, no opinions, no arithmetic to perform. The outcome is to recognize, interpret and question a water number, not to calculate a full SWI.
Draft status stated everywhere
The Software Water Intensity specification is draft v0.1.0, a work in progress and subject to change. Every module says so where it cites the spec.
One worked example, carried through
Northbridge, the same constructed organization used across the catalogue, so the figures stay consistent from module to module.
What this course helps with
The dimension most teams have not started on
Carbon has a ratified standard and years of practice behind it. Water is newer, the specification is still in draft, and most software teams have never been asked the question. This course is the entry point.
Numbers that arrive without a basis
One service reports 12 mL per request, another 9 mL. Module 4 walks that comparison and reaches the honest answer: not comparable as stated, and here is exactly why.
A liter is not a liter
The same volume means something different in a water-stressed basin. Module 5 explains stress adjustment, and why the AWARE and Aqueduct scales cannot be swapped for one another.
Who this is for
- Every software role — developers, testers, architects, data and platform engineers, product and delivery
- Sustainability practitioners adding water to work that so far covers only carbon
- Anyone who has been handed a water figure and asked whether it can be used
- No prerequisites and no engineering seniority assumed
Related courses:
This is a cross-role feeder course. For producing footprint figures rather than reading them, see SCI for AI Practitioner: The Measurement Lab. For the placement and capacity decisions that change a water number, see Green and Efficient Architect and GreenOps Practitioner.
What you will work with
The four layers
Building, electricity, hardware and the stress conversion — the structure everything else in the course hangs from.
The Northbridge worked entry
1,000 kWh in a month at a WUE of 0.3 L/kWh gives 300 L direct; a grid factor of 1.5 L/kWh gives 1,500 L indirect; across 100,000 requests that is roughly 18 mL per request. Illustrative figures — and quantified operational water only, embodied not yet quantified, so a partial result rather than a complete SWI.
The reading card
Eight questions to ask of any water figure, from how much and which kind to what is missing, ending with whether it can be compared or filled in for the Northbridge entry.
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
