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Data and Models

Write prompts that get the answer first time, at the smallest footprint that does the job.

Eight modules on the Prompt Footprint and the Five Levers: specification, structure, reasoning depth, model selection and orchestration. They apply to the prompts you write yourself and to the agents that run without you.
8h 30m
of learning
8
modules
50 question
final assessment
85%
to pass
Certification
upon completion

Green AI Prompting for Self & Agents

What
How
Tools
Who

How the course works

Five levers, applied in order

Specification, structure, reasoning depth, model selection and orchestration, each with the question to ask and the evidence to check.

For your own prompts and for agents

The same levers applied to what you write and to what runs autonomously, which behave differently at scale.

Grounded in SCI for AI

Footprint figures follow the ratified specification, with method and boundary stated.

What this course helps with

Understand what a prompt actually costs

A prompt is authored once and runs on every request. The first module accounts for that across cost, carbon, energy, water and embodied hardware.

Get the answer first time

Vague requests are corrected, and every correction resends the whole conversation for a fresh answer. Specification is the lever that removes most of that work.

Keep agents inside a budget

Autonomous work multiplies calls and carried context. Budgets and stop conditions put a bound on the worst case.

Who this is for

  • Developers and software engineers working with AI features

  • Prompt engineers and AI product managers

  • ML engineers and architects designing AI features

  • Anyone whose prompts run at production volume


  • Recommended first: LFC131 Green Software for Practitioners (not required)

Related courses:

For the training side of models rather than their use, see Green AI for ML Practitioners; for putting this guidance into coding tools, see Building a Green AI Agent.

What you will work with

The Prompt Footprint

Five dimensions of a single prompt, and what changes when it runs a million times.

The five levers

Each with its GSF framework, a backwards audit for prompts already running, and the quality bar it must hold.

Procurement and disclosure

What to ask an AI supplier, and how to report your own figures without overstating them.

What you will be able to do

  • Account for a prompt's footprint across cost, carbon, energy, water and hardware, per call and at scale
  • Apply the Intent Framework so a task succeeds the first time
  • Control output length, and match reasoning depth to how hard the task is
  • Route work to the smallest model that meets a written quality bar
  • Set limits on agent work with the four Orchestration Guardrails
  • Measure and report a prompt's footprint as a Software Carbon Intensity for AI (SCI for AI) score, with its method and boundary

Course Lessons

The full course

Every module ends with an application exercise and a worked solution.

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 & Credential

How you are assessed

  • A short knowledge check after every module, drawn at random from that module’s pool
  • A final assessment of 50 scenario-judgement questions from a pool of 80
  • 85% to pass, 120 minutes, retake available
  • Questions test judgement in situations you will meet — not recall of figures that date

What you earn

GSF Certification
Green AI Prompting
  • Issued on passing the final assessment
  • Valid for 2 years, refreshed by a short update module
  • Shareable credential for your profile, CV and tender responses

Time to complete

Reading and exercises
8h 30m across 8 modules
Knowledge checks
About 5 minutes per module
Final assessment
120 minutes
Typical completion
About 11h including knowledge checks and the final, at a module per session

Join the Pilot

The GSF Academy is currently in pilot. If your organization would like to enrol learners and issue GSF certifications to your teams, tell us what you need, and we'll follow up to discuss access, pricing, and enrolment.

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