The Consultant-Grade AI Interaction Model: Professional Leverage.
The 6-Layer Interaction Stack
Layer 1 – Intent (Why This Exists)
Bad prompts ask what. Consultant prompts declare purpose, scope, and consequence.
Consultant framing
- Decision to be supported
- Risk to be reduced
- Outcome to be defended
Example
“I need to determine whether this programme is salvageable before committing political capital.”
Layer 2 – Authority (Who Is Speaking)
When authority and accountability are explicit, AI responses shift from informational to judgement-oriented.
Explicitly define
- Role (e.g. Programme Director, Partner, CFO)
- Perspective (delivery, commercial, governance)
- Accountability level (advisory vs decision-maker)
Result
- Fewer disclaimers
- Stronger judgement
- Clearer trade-offs
Layer 3 – Context (What AI Is Allowed to Assume)
Undisciplined prompts supply information.Consultant-grade prompts define the operating constraints within which the response must hold.
Include:
- Organisational maturity
- Stakeholder dynamics
- Delivery constraints
- Cultural friction points
Key rule
Context is constraints, not background noise.
Layer 4 – Framing (How the Problem Is Shaped)
Consultant-grade prompts define the analytical structure the response must follow, rather than requesting unstructured answers.
AI should be instructed to:
- Use frameworks
- Surface failure modes
- Highlight second-order effects
- Separate signal from noise
Example
“Structure this as: risks, root causes, non-obvious implications, and executive options.”
Layer 5 – Output Contract (What ‘Good’ Looks Like)
Consultant-grade prompts explicitly define the form, depth, audience, and standard of the output before it is produced.
Define:
- Audience (ExCo, Board, delivery team)
- Depth (one-pager vs working paper)
- Tone (neutral, assertive, cautionary)
- What not to include (e.g. no buzzwords, no generic advice)
Consultant rule
If the output can’t be lifted into a deck or paper, it’s not done.
Layer 6 – Challenge Loop (AI as Thinking Partner)
Consultant-grade prompts require the AI to challenge assumptions, surface counter-arguments, and identify non-obvious risks before conclusions are accepted.
Instruct the AI to:
- Challenge assumptions
- Flag weak logic
- Offer alternative framings
- Identify what you might be underestimating
Example
“Tell me where this logic would not survive scrutiny from a sceptical CFO.”
The Anti-Patterns (What This Model Explicitly Avoids)
🚫 Prompt stuffing 🚫 “Explain like I’m five” framing 🚫 Tool-driven outputs (“use SWOT because SWOT”) 🚫 Generic best practice lists 🚫 Faux confidence without evidence
The Consultant-Grade Prompt Formula
“Act as [senior role]. You are operating within [constraints and context]. The decision at stake is [decision]. Structure your response as [framework]. Assume the audience is [audience]. Challenge my assumptions and highlight non-obvious risks.”
That single pattern will outperform 90% of prompt libraries. This thinking underpins the work I’m doing around consultant-grade AI interaction models — focused on precision, accountability, and outputs that survive executive scrutiny.
In practice, the model is reinforced by applied guardrails that sit above the prompt itself — requiring explicit assumptions, verifiable sources or conditional logic, hallucination checks, and human review — so that AI outputs inform judgement without silently introducing risk.
Best regrads RichFM
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