Each principle deepens one aspect of The Middle Principle: purpose, context, acceleration, verification, active judgment, responsibility and human value at both ends.
Principle 01
Purpose before prompt
A clear objective matters more than clever phrasing.
Definition
Direction begins with deciding what the work is for. Prompt craft is useful only after purpose, audience and success criteria are clear.
Rationale
Without purpose, AI optimises for plausible text rather than meaningful outcomes. Clarity of intent reduces waste and misalignment.
Example
A school leader first defines why an AI-use policy is needed, who it serves and what risks it must address, then asks AI to help draft.
Counterexample
A team asks AI to 'write a policy' with no audience, scope or non-negotiables, then accepts the first fluent draft.
Reflection: Can I state the purpose of this work in one sentence before I open an AI tool?
Principle 02
Context before generation
AI quality depends on the relevance and completeness of the information it receives.
Definition
Useful acceleration requires grounded context: constraints, evidence, audience, tone and what must not be ignored.
Rationale
Models cannot invent your organisation's reality. Incomplete context produces confident but misaligned answers.
Example
A writer provides audience, argument, sources and voice notes before requesting outline options.
Counterexample
A manager pastes a vague request with no background and treats the generic reply as organisational guidance.
Reflection: What context would a skilled colleague need before helping with this task?
Principle 03
Acceleration is not authority
Fast output should not be confused with expert judgment.
Definition
AI can increase speed, breadth and iteration. It does not acquire professional authority, legal standing or moral accountability.
Rationale
Fluency and formatting create a false sense of completion. Authority remains with the human who accepts the outcome.
Example
A clinician uses AI to organise evidence, then applies clinical expertise before any decision.
Counterexample
A team treats AI-generated recommendations as settled strategy because they arrived quickly and looked polished.
Reflection: Am I treating speed as a substitute for expertise?
Principle 04
Verification before trust
Confidence, fluency and formatting are not evidence of accuracy.
Definition
Judgment requires checking claims, sources, omissions and suitability before the work is used or published.
Rationale
Generative systems can be wrong while sounding right. Verification is the bridge between draft and decision.
Example
A marketer fact-checks claims, compares wording against brand standards and removes unsupported assertions.
Counterexample
A report is shared because it reads well, without checking citations or contested claims.
Reflection: Which claims in this output would I be unwilling to defend without checking?
Principle 05
Judgment must be active
Human review means challenging and improving the work, not merely approving it.
Definition
Judgment is evaluative work: questioning, editing, rejecting, refining and deciding. Passive approval is not judgment.
Rationale
Ceremonial review creates the appearance of responsibility without the substance of it.
Example
A developer reviews AI-drafted code for security, maintainability and system impact, then rewrites critical sections.
Counterexample
A leader clicks approve on an AI summary without reading the source evidence.
Reflection: What specifically did I challenge, change or reject in this output?
Principle 06
Responsibility cannot be delegated
A tool cannot be accountable for the consequences of a decision.
Definition
People and organisations remain responsible for outcomes, even when AI contributed to the process.
Rationale
Accountability is a human and institutional obligation. Blaming the tool dissolves ownership where it is most needed.
Example
A policy author records ownership, review steps and final approval, regardless of AI drafting support.
Counterexample
An organisation attributes an error to 'the AI' and fails to identify who accepted the work.
Reflection: Whose name sits against this outcome if something goes wrong?
Principle 07
Human value belongs at both ends
Humans contribute purpose at the beginning and meaning at the end.
Definition
The distinctive human contribution is not only oversight after generation. It is also the definition of purpose before AI enters.
Rationale
If humans only appear at the end, they inherit someone else's framing. Direction protects agency; judgment protects quality.
Example
A team defines strategic questions first, uses AI to explore scenarios, then decides what the organisation will do.
Counterexample
Work begins with AI generation and humans only tidy the language afterwards.
Reflection: Where did human thinking begin and end in this piece of work?
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