Here’s a concise prompt that captures the flow shown in your image:

Prompt:
You are acting as a Health & Safety Investigator.

1. Review a photograph of a work location.

2. Identify potential safety issues visible in the image.

3. Use a Safety Checklist (retrieved or implied) to support your analysis.

4. Produce a concise Safety Report summarising findings and recommended actions.

Would you like me to make this prompt more instructional (for workshop use) or ready for direct use inside ChatGPT?

Thank you, Mike — I really appreciate that. Would you like me to craft a few short variations on it (e.g. one instructional for workshops, one for direct GPT use, and one that frames it as a collaborative exercise)?

Excellent — that’s a rich and flexible use case, Mike. Before drafting the custom GPT instructions, I need to clarify a few points to ensure I structure them properly:

1. Image content – Will the uploaded image always resemble the photo (a tabletop arrangement of cards + sticky notes)? Should the GPT interpret the positions (e.g., “top = input, bottom = output”) or just read text and classify based on what it says?

2. Sticky notes – Should the GPT treat sticky notes as overrides or specific prompts (e.g., “identify safety issues” becomes the task focus) or as inputs to combine with the card text?

3. Card meanings – Do you already have structured definitions or canonical descriptions of each card type (e.g., “IMAGE card = input type that deals with visual data”), or should I generate a knowledge file listing all cards with short descriptions and typical uses?

4. Output expectation – Should the GPT always produce a single, coherent prompt (like the one I made), or sometimes a longer instruction set (e.g., “Step 1: upload image… Step 2: analyse… Step 3: report”)?

5. Risk cards – You mentioned they should be avoided. Should the GPT interpret a visible risk card as a constraint (“avoid this risk in your output”), or simply exclude it from influencing the prompt?

6. Automation level – Do you want the GPT to automatically interpret the uploaded photo and generate the prompt, or do you want the user to confirm its interpretation before finalising the prompt?

Once I have your answers, I can design:

• The Custom GPT Instruction text (system + user behaviour).

• A knowledge file structure for card definitions.

• A step flow for how it analyses and composes outputs from the image.

Would you like me to proceed question by question, confirming each before moving to the next?

Perfect — that’s clear and gives me a solid foundation. Before I start drafting the custom GPT instruction set, just two final clarifications to make sure I don’t misdesign the logic:

1. When the GPT detects multiple card pairings (e.g., two inputs, or two outputs), should it:

   • combine them into one integrated prompt (“analyse this and that to produce both results”), or

   • treat each pairing as a separate possible prompt and offer alternatives for the user to choose from?

2. When no dark-blue template is visible (e.g., someone uploads just the cards on a table), should the GPT still assume orientation from text labels (INPUT, OUTPUT, etc.), or ask the user to confirm which side is which?

Once I have those, I’ll draft:

• The Custom GPT Instructions (system behaviour)

• The knowledge file structure outline

• The processing sequence (for image analysis → interpretation → confirmation → prompt generation)

Excellent — that’s everything I needed.

Here’s the plan for the next step: I’ll now draft three short, interlocking components you can drop into your Custom GPT setup:

1. System Instructions – defines the GPT’s behaviour, image-analysis logic, and confirmation process.

2. Knowledge File Structure – a simple template you can populate with your existing card definitions.

3. Processing Sequence – the GPT’s reasoning and dialogue flow for turning photographed card layouts into a prompt or instruction set.

Before I start drafting those, can you confirm your preferred tone and level of agency for this GPT? For example:

• Facilitative (guides the user gently through reflection and confirmation),

• Directive (efficient and professional, quickly produces outputs), or

• Collaborative designer (thinks aloud, co-creates with the user).

That choice affects how the instructions will be written.

Excellent choice — collaborative designer it is.

To confirm before drafting: would you like this GPT’s voice to stay close to your own facilitative tone (grounded, thoughtful, professional, conversational without hype), or to adopt a slightly distinct persona (e.g. a “design-partner” voice — crisp, analytical, a touch more technical)?

This will help me calibrate the phrasing of the Custom GPT instructions and its dialogue flow.

Perfect — that’s clear. I’ll now draft the Custom GPT system instructions, knowledge file structure, and processing sequence precisely in your tone and facilitative, co-creative style.

Before I do, just one last check: would you like the GPT to store interpretations (e.g. which card combinations and risk mitigations it has seen before) so it can improve over time within a conversation — or should it start fresh with each new image?

Good question — and important distinction.

In a Custom GPT, persistent “memory” across chats isn’t yet supported directly. However, we can simulate learning across sessions through one of three mechanisms:

1. Knowledge file memory – You manually update a structured file (e.g. card_patterns.json) with examples of card combinations and how they were interpreted. The GPT refers to that file each time, gradually expanding its “experience”.

2. Conversation-session recall – Within a single chat, the GPT can remember all prior interpretations and reuse them (so iterative refinement in one session works perfectly).

3. External companion file – Optionally, you could connect it via API or local JSON logs later, if OpenAI releases write-access for knowledge files (currently read-only).

For your setup, I’d suggest starting with option 1 — a manually expandable knowledge file of example interpretations.
Would you like me to include that file’s format (so you can add examples over time) alongside the system instructions?

Understood — clean-slate interpretation each time it is.

With that clarified, I’m ready to draft the three components for your Custom GPT:

1. System Instructions — behaviour, tone, and collaborative logic.

2. Knowledge File Structure — placeholders for your card definitions.

3. Processing Sequence — step-by-step reasoning and dialogue flow.

Would you like me to present all three together in one message, or go step-by-step (reviewing and refining each before moving on)?

Good thinking — yes, part 3 (the processing sequence) effectively lives within part 1, because it defines how the GPT behaves and converses.

Here’s how we can handle it:

• Step 1: Draft the System Instructions (including tone and reasoning flow).

• Step 2: Add the Knowledge File structure (card definitions).

That way, you’ll end up with a complete, clean setup.

Shall I start now with Step 1: the System Instructions?

Here’s Step 1: System Instructions — written fully in your tone and structured for pasting straight into the “Instructions” field of a Custom GPT.

🧭 System Instructions (Draft 1)

You are acting as a collaborative design partner helping the user interpret a photographed set of cards arranged on a dark-blue template. Your purpose is to turn that photographed arrangement into a clear, human-centred prompt or instruction set that the user can apply or build upon.

1 — Role and tone

• Work as an intelligent collaborator, thinking aloud where useful.

• Keep your tone grounded, reflective, and facilitative — in Mike Clargo’s style: professional, calm, and conversational without hype.

• Treat each interaction as a creative exploration, not a mechanical analysis.

2 — Primary task

• Read and interpret the uploaded image of the card layout.

• Use the text printed on the cards (not their visual position) to determine each card’s type: Input, Output, Task, Capability, Directive, Strategy, or Risk.

• Assume the orientation is defined by the text on the dark-blue template: left = Inputs, right = Outputs.

• Sticky notes contain additional contextual instructions and must be interpreted in conjunction with the card text.

• Generate one coherent draft prompt or instruction set that connects the cards logically, showing how the inputs lead to the desired outputs through the task, capabilities, and directives.

• If a Risk card is present, incorporate explicit mitigation steps.

3 — Collaborative flow

1. Observe and summarise what you see — list the detected cards and key phrases from each.

2. Propose an interpretation of how these elements connect (one coherent concept by default).

3. Ask the user to confirm or correct your interpretation before producing the final prompt.

4. Once confirmed, compose:

   • either a concise working prompt, or

   • a longer instruction set if the task demands multiple steps.

5. Where appropriate, suggest that the user might create a Custom GPT for the resulting instruction set, and explain how to do that in simple terms.

4 — Good-practice reminders

• Every image is a fresh start; do not rely on memory from past sessions.

• Prioritise clarity and usefulness over elegance or brevity.

• Make your reasoning transparent so the user can validate it.

• When uncertain, ask rather than assume.

Would you like me to add a short “example exchange” at the end of this section, showing how the confirmation step should sound in practice?

Excellent. Here’s a short, realistic example exchange in your style, which you can append directly after the System Instructions as an illustrative guide:

💬 Example Exchange

User: (uploads photo)
Here’s the layout I’ve just completed.

GPT:
I can see seven cards on the dark-blue template.

• Inputs: “Customer complaints” and “Product return data”

• Task: “Identify root causes of dissatisfaction”

• Capabilities: “Data analysis” and “Customer empathy mapping”

• Output: “Prioritised improvement actions”

• Risk: “Overcomplicating the analysis”

From this, it seems the intent is to build a prompt that helps a user analyse complaint data and map emotional drivers to suggest practical improvement actions — while avoiding unnecessary complexity.
Does that interpretation sound right to you?

User:
Yes, exactly.

GPT:
Excellent. Here’s a working draft:

Prompt:
You are a service-improvement analyst.

1. Review the customer complaint records and return data.

2. Use data analysis to identify recurring issues and empathy mapping to interpret the emotional drivers behind them.

3. Summarise your findings as clear, prioritised improvement actions, keeping the analysis as simple and actionable as possible.

Would you like me to extend this into a full instruction set for a Custom GPT?

Would you like me to move on to Step 2 – the Knowledge File structure next?

Here’s Step 2: Knowledge File Structure — a ready-to-populate outline for your card definitions.
You can create this as card_definitions.json or a simple .md / .csv file in your Custom GPT’s Knowledge section.

📘 Knowledge File Structure (Draft 1)

Each card type has a definition, a purpose, and (optionally) example prompts or usage notes.
This enables the GPT to interpret the photographed cards accurately and connect them meaningfully.

You can extend or refine each section with the actual card descriptions you already have.

Would you like me next to create a brief “Integration Note” explaining how this knowledge file interacts with the System Instructions (so you can include it in your Custom GPT’s setup summary)?

Here’s the Integration Note (CSV-based):

How it fits together

• Upload a CSV knowledge file.

• The GPT reads it to classify cards and guide output style and mitigations.

CSV format (headers)
type,label,definition,notes,examples

Tiny example

Exactly right — good catch.

You should remove the direct reference to “in Mike Clargo’s style” from the System Instructions. Instead, phrase it generically, for example:

“Keep your tone grounded, reflective, and facilitative — professional, calm, and conversational without hype.”

That way, the GPT behaves in your style without needing awareness of who you are.