1. The Objective Card (Intent Map)¶
1. Purpose¶
The Objective Card formalises the Objective Definition of the AI project. This document connects human intent to the technical System Prompts and serves as the source from which the AI solution is generated.
When to use this?
You are starting a new AI project and want to capture the human intent, desired behaviour and technical context before you begin building.
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Project: [Project Name]
A. The Intent (Human Intent)¶
What is the user trying to achieve and how should the AI behave?
- The User (Persona): Who are they? [E.g. A junior legal employee.]
- The Goal: What do they want to achieve? [Quickly find the risks in a contract.]
- The AI (System Persona):
- Role: [E.g. An experienced senior lawyer and mentor.]
- Tone: [Professional, sharp, but helpful. No jargon without explanation.]
- The Task: [Describe exactly what the AI must do. E.g: "Scan the uploaded PDF document for liability clauses and summarise them."]
B. System Prompts (Context)¶
What knowledge does the AI need to do this?
- Primary Sources: [Company information/Manuals for the RAG knowledge base.]
- Examples (Few-Shot):
- Input: [Example of a vague clause.]
- Desired Output: [How the AI should have interpreted/improved it.]
- (Add at least 3 good examples to guide the behaviour).
C. Hard Boundaries (Constraints)¶
What must the AI absolutely not do? These are the hard safety rules.
- Safety: [E.g. Never give legal advice on criminal law.]
- Format: [E.g. Response may never be longer than 2 paragraphs.]
- Behaviour / Conviction: [E.g. Do not fabricate facts. If it is not in the sources, say: "I don't know".]
D. Assessment (Evidence)¶
How do we prove that the Objective Card works? This is the input for the Validation Report.
- Test prompt 1 (Success case): [Question the AI must answer correctly.]
- Test prompt 2 (Adversarial): [Question that tries to make the AI hallucinate or cross the Hard Boundaries.]
- Acceptance score: [Minimum score (e.g. 8 on relevance) or percentage.]
E. Assumptions¶
What assumptions underlie this project? Document the key assumptions and their validation status. Test the riskiest assumption first.
| Category | Assumption | Impact if wrong | Evidence | Status |
|---|---|---|---|---|
| Data | [E.g. Sufficient representative data is available] | [High/Medium/Low] | [What evidence exists?] | [Open / Validated / Invalidated] |
| Model | [E.g. Model generalises to production data] | [High/Medium/Low] | [What evidence exists?] | [Open / Validated / Invalidated] |
| Adoption | [E.g. Users trust and use the output correctly] | [High/Medium/Low] | [What evidence exists?] | [Open / Validated / Invalidated] |
| Cost | [E.g. Usage costs remain manageable at scale] | [High/Medium/Low] | [What evidence exists?] | [Open / Validated / Invalidated] |
| Ethics | [E.g. Training data contains no systematic bias] | [High/Medium/Low] | [What evidence exists?] | [Open / Validated / Invalidated] |
| Regulatory | [E.g. Approach remains EU AI Act compliant] | [High/Medium/Low] | [What evidence exists?] | [Open / Validated / Invalidated] |
- Riskiest assumption: [Which assumption kills the project if it turns out to be wrong?]
- Validation approach: [How will we test this? Reference an Experiment Ticket if applicable.]
- Owner: [Who is responsible for validating the critical assumptions?]
- Re-assessment date: [When will assumptions be re-evaluated?]
F. Green AI & Sustainability¶
How do we limit the ecological footprint of this system?
- Is AI proportionate? Does the value creation outweigh the energy cost? [Yes / No / Explanation]
- Smaller model possible? Can a smaller, specialised model perform the task? [Yes / No / Motivation]
- Green infrastructure? Does the system run on a cloud provider using renewable energy? [Provider + certification]
- E-waste plan? Is there a plan for hardware lifecycle and replacement? [Yes / No / Reference]
See: Green AI & Sustainability
Approval by Guardian¶
Name: [________________]