How to use these frameworks
These frameworks are designed to make AI your thought partner, not your replacement. The structure exists to draw out better thinking, not to slow you down.
Getting started
Data privacy reminder
If you are sharing sensitive business information, confidential data, or anything that should not be in the public domain, check your AI tool's privacy settings first. Most providers offer options to prevent your conversations being used for model training. In ChatGPT, disable "Improve the model for everyone" in Settings → Data Controls. In Claude, check your workspace settings. Taking a moment to configure this protects your organisation's information.
- 1Find a framework that matches your challenge using search or browse
- 2Click the copy button to copy the full framework
- 3Paste into your AI tool of choice: ChatGPT, Claude, Gemini, or any other LLM
- 4Answer the interview questions to provide your specific context
- 5Review the output and refine through follow-up conversation
These frameworks work with any modern AI assistant. They have been tested primarily with Claude and ChatGPT, but the structure works across platforms.
Pro tip: Use voice-to-text
These frameworks draw out substantial context from you, and typing detailed responses can slow you down. Voice-to-text tools let you speak your answers naturally, which is faster and often produces richer context.
We recommend Wisprflow which works across any application on your computer. Use this link for a free month to try it out.
Why structured frameworks outperform one-liners
A simple request like “Write me a business case” will give you something generic. The AI has no context about your situation, your constraints, or what good looks like in your organisation. It fills the gaps with assumptions, and those assumptions are usually wrong.
The frameworks in this library work differently. They establish credibility through a practitioner role, define the specific task, request the context that actually matters, and specify a structured output that covers what experienced operators know to include.
The result is an output tailored to your situation, structured the way a seasoned professional would approach the problem, with the nuance that comes from understanding your specific context.
The anatomy of a Sqwyz framework
Practitioner role with credibility
Opens with a senior role and specific experience. This shapes how the AI approaches the problem, drawing on patterns from similar situations rather than generic advice.
Context inputs that matter
Specifies what information the AI needs to give you a tailored answer. These are the inputs an experienced consultant would ask for before giving advice.
Structured output specification
Defines what the deliverable should cover. The sections reflect what practitioners know to include, the things that get missed when you are rushing or new to a domain.
Interview instruction
Tells the AI to ask questions before producing output. This ensures the response is based on your actual situation, not assumptions.
About the interview questions
Every framework ends with an instruction for the AI to interview you before producing output. This is where the real value lies. The questions force you to think through your situation properly, and they give the AI the context it needs to be genuinely useful.
Think of it as having a conversation with a knowledgeable colleague who asks the right questions before jumping to conclusions. The process of answering often clarifies your own thinking.
Need a faster answer?
If you are time-pressed or already know exactly what you need, you can remove the interview instruction at the end of the framework. Simply delete everything after the horizontal line (---).
When you do this, paste the framework and then immediately provide your context in the same message. The AI will produce the output directly. You will get a faster response, though it may be less tailored than if you had gone through the interview.
A thought partner, not a replacement
The goal of these frameworks is not to have AI do your job. It is to give you a thinking partner who can help you structure problems, challenge your assumptions, and produce first drafts that you can build on.
The best results come when you treat the output as a starting point. Read it critically. Push back where the AI has made assumptions that do not fit your context. Use it to identify gaps in your own thinking.
AI is good at structure, synthesis, and generating options. You bring judgment, context, and accountability. The combination is more powerful than either alone.
Example framework structure
You are a Programme Director who has kicked off dozens of complex initiatives across retail, financial services, and private equity portfolio companies. You understand that the first 30 days of a programme set the trajectory for everything that follows.
You are helping structure the discovery phase of a new programme.
I will share:
- Programme objectives and success criteria
- Key stakeholders and their interests
- Known constraints and dependencies
- Timeline expectations
- Available resources and budget parameters
Please provide a discovery phase plan covering:
1. **Stakeholder Landscape**
- Key decision-makers and their priorities
- Influencers and potential blockers
- Communication preferences and cadence
2. **Current State Assessment**
- Information sources to review
- Interviews to conduct
- Data to gather
[... continues with structured sections ...]
The quality of your discovery phase determines whether you spend the programme fighting fires or building momentum.
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Before producing any output, interview me one question at a time to gather the context you need. Ask only one question, wait for my response, then ask the next. Continue until you have enough information to produce a high-quality, tailored output.Ready to try it?
Browse the library to find frameworks for your current challenge. Copy, paste into your AI tool of choice, and let the interview begin.