🚀 AI Innovation Project #001 | Question Discovery Assistant
What if AI stopped giving answers?
This provocation from PO Prompts #001 opens an interesting possibility:
An AI assistant that helps us discover better questions before rushing to answers.
The proposed project:
Build an AI-powered Question Discovery Assistant that:
Challenges hidden assumptions
Identifies missing evidence
Reveals overlooked perspectives
Suggests questions and experiments
Helps improve decision-making
The experiment: Compare decisions made using conventional AI answers with those supported by the Question Discovery Assistant.
Inspired by Edward de Bono's Serious Creativity, the project demonstrates how lateral thinking can take us beyond mere AI-generated ideas.
Lateral thinking discovers new directions. AI helps turn them into accomplishments.
🎬 Watch the 15-second concept video:
https://youtube.com/shorts/41NMZBbng-E
💬 Would you use an AI assistant that asks better questions instead of immediately giving answers?
PO Prompts — Think the Unthinkable.
AIYING — Accomplishing with AI
#POPrompts #LateralThinking #AIInnovation #GenerativeAI #AIYING
Technical Blueprint: Building the AI Question Discovery Assistant
Project objective: Develop an AI assistant that improves human reasoning by asking insightful questions, challenging assumptions and identifying missing evidence before recommending solutions.
1. Recommended Technology Stack
| Component | Recommended tool | Purpose |
|---|---|---|
| AI model | OpenAI GPT models | Generate insightful questions, examine assumptions and analyze responses |
| Prototyping | ChatGPT Custom GPT | Build an initial no-code prototype |
| Programming | Python | Implement the application's logic |
| User interface | Streamlit | Create an interactive web application |
| AI integration | OpenAI API | Connect the application to an AI model |
| Workflow management | LangGraph (optional) | Coordinate multi-step questioning and evaluation |
| Data storage | SQLite | Store questions, responses and experiment results |
| Evaluation | Python, pandas | Analyze question quality and decision outcomes |
| Deployment | Streamlit Community Cloud | Publish a prototype for testing |
Recommended starting point: Build a Custom GPT first. Move to Python, Streamlit and the OpenAI API when greater control, structured logging or systematic evaluation becomes necessary.
2. Proposed System Architecture
User Problem → Assumption Discovery → Question Generation → Evidence Identification → Reflection → Decision Support
The assistant operates through five stages:
Understand: Identify the user's problem, objective and decision context.
Challenge: Surface hidden assumptions and alternative interpretations.
Question: Generate five relevant, non-obvious questions.
Investigate: Identify missing evidence and suggest practical ways to obtain it.
Support: Help the user develop and evaluate possible decisions without pretending uncertain information is established fact.
3. Prototype Prompt
Create a Custom GPT using the following instruction:
You are a Question Discovery Assistant inspired by lateral-thinking principles.
Your primary responsibility is not to provide immediate answers but to improve the user's understanding of the problem.
For each problem:
Identify the underlying objective.
Surface three potentially limiting assumptions.
Ask five insightful questions, including at least one that challenges the conventional framing.
Identify important missing evidence.
Suggest two practical investigations or experiments.
Provide recommendations only when requested, clearly separating evidence from assumptions.
Be constructive, concise and curious. Never invent supporting evidence.
4. Minimum Viable Product (MVP)
Build a Streamlit application with:
A text box for entering a problem or decision.
A button to discover assumptions and questions.
A panel displaying five AI-generated questions.
An option to answer the questions and refine the analysis.
An evidence checklist.
An exportable decision summary.
Example input: "Should our college introduce AI-based examinations?"
Instead of immediately recommending yes or no, the assistant might ask:
What educational outcome are examinations supposed to measure?
Which assessment practices are we assuming must remain unchanged?
What evidence would show that AI improves or weakens learning?
Could assessment become continuous rather than examination-based?
What small pilot could test an alternative fairly?
5. Evaluation Experiment
Compare two approaches using the same set of decision problems.
Group A: Conventional AI assistant providing direct recommendations.
Group B: Question Discovery Assistant encouraging structured exploration before recommendations.
Evaluate:
Relevance and originality of questions.
Identification of hidden assumptions.
Quality of supporting evidence.
Consideration of alternative solutions.
Quality of the final decision rationale.
Time and effort required.
Use independent reviewers and a predefined scoring rubric. Avoid assuming that asking more questions automatically produces better decisions.
6. Suggested Development Roadmap
Phase 1 — No-code prototype: Build and manually test a Custom GPT.
Phase 2 — Application prototype: Implement the workflow using Python, Streamlit and the OpenAI API.
Phase 3 — Evaluation: Test 10–20 representative decision scenarios, compare against a direct-answer baseline and review failure cases.
Phase 4 — Improvement: Refine questioning strategies, add evidence checks and strengthen privacy and safety controls.
Phase 5 — Pilot: Invite a small group of students, teachers or professionals to evaluate the application.
Expected Deliverables
Working Question Discovery Assistant.
Reusable prompting framework.
Interactive web prototype.
Evaluation rubric and test dataset.
Pilot report documenting strengths, limitations and improvement opportunities.
Beyond Mere Generation
The distinctive feature of this project is not simply generating more questions.
It is deliberately challenging existing thinking patterns, using lateral-thinking techniques to discover new directions, and employing AI to investigate those directions.
Lateral thinking is the creative engine. AI is the exploration amplifier. Accomplishment is the destination.
PO Prompts — Think the Unthinkable.
AIYING — Accomplishing with AI
