#3813 Problem Statement Innovative Thinking Methods Web App Requirements

#3813 Problem Statement Innovative Thinking Methods Web App Requirements

#3813 Help me define requirements for a Gen-AI web app. The primary input is a user-specified problem statement. A prompt will be submitted with the problem statement with a request to evaluate the problem statement and suggest the best sequence of innovative thinking methods to apply to the problem statement. The required output will be a JSON list of innovative thinking methods, for which the app will iterate through those methods, submitting a prompt to apply each method to the problem statement. Finally, rate the feasibility and practicality of the best solutions(s). Export of the prompt/response chain shall be supported.

That sounds like an excellent application for harnessing the power of prompt chaining! It formalizes the ideation process and makes it repeatable.

Here is a formal requirements document outlining the functional, technical, and user experience requirements for your Gen-AI innovation web application.

Requirements Document: Gen-AI Innovation Accelerator

1. Overview and Goal

The goal of the Gen-AI Innovation Accelerator application is to systematically guide users through structured innovative thinking methodologies. By breaking down the problem-solving process into a sequence of LLM interactions (prompt chaining), the application will ensure comprehensive exploration of a problem space, resulting in prioritized, feasible solutions.

2. Functional Requirements (FR)

These requirements define the specific, chain-based interactions with the Large Language Model (LLM) using the gemini-2.5-flash-preview-09-2025 model.

ID	Feature Name	Input(s)	LLM Process & Output Format	LLM Role / Persona
FR-1	Method Suggestion & Structuring (Step 1)	User Problem Statement (Text)	Process: Evaluate the problem complexity, domain, and scope. Suggest a sequence of 3-6 optimal innovative thinking methods (e.g., SCAMPER, 5 Whys, Design Thinking Stages). Output: A strict JSON array of method names.	Innovation Methodologist: Expert in creative problem-solving frameworks.
FR-2	Method Execution Iteration (Step N)	1. User Problem Statement (Text) 2. Current Method Name (from FR-1) 3. Previous Step Outputs (for context/consistency)	Process: Apply the specific method to the problem statement. Generate concrete ideas, insights, and questions relevant to that method. Output: Generated Text (Formatted for easy reading, e.g., using bullet points or numbered lists).	Creative Facilitator: Guides the user through the specified thinking framework.
FR-3	Solution Synthesis & Rating (Final Step)	1. User Problem Statement 2. All Generated Solutions/Insights (Text from all FR-2 steps)	Process: Synthesize the most promising solutions (top 3) generated across all methods. Rate each based on two criteria: Feasibility (can we build it?) and Practicality (should we build it?). Output: Structured Markdown/Text summarizing the top solutions with their ratings.	Feasibility Analyst: Objective, critical evaluator of potential business solutions.

3. Technical Requirements (TR)

ID	Requirement	Description
TR-1	LLM API Usage	All interactions must use the non-streaming generateContent endpoint with the gemini-2.5-flash-preview-09-2025 model.
TR-2	JSON Output Enforcement	The LLM call for FR-1 (Method Suggestion) must enforce a specific JSON schema using responseMimeType: "application/json" to guarantee the next steps are executable by the application logic.
TR-3	Prompt Chaining Logic	The application must programmatically manage the sequence: User Input -> FR-1 Call -> Parse JSON -> Loop FR-2 Calls (sequentially) -> FR-3 Call.
TR-4	API Retries	Implement an exponential backoff strategy for all LLM API calls to handle potential rate limits or transient errors.
TR-5	State Management	Use Firebase Firestore to store the user's current session state, including the original problem, the determined method chain, and all intermediate LLM responses.

4. User Experience Requirements (UXR)

ID	Requirement	Description
UXR-1	Primary Input Field	A clear, multi-line text area for the user to input the "Problem Statement."
UXR-2	Workflow Visualization	A visual display (e.g., a roadmap or list) showing the status of each step in the chain (Suggested Methods, Executing: Method X, Complete).
UXR-3	Real-Time Output Display	Display the LLM's generated content (FR-2 and FR-3) in real-time as each step completes.
UXR-4	Export Functionality	A button/feature allowing the user to export the entire session log, including the Problem Statement, the full prompt for every LLM call, the resulting output text, and the final synthesis (FR-3). The export should be available in a readable format (e.g., Markdown or structured JSON/PDF).
UXR-5	Loading Indicator	A prominent loading indicator must be displayed while any LLM call (FR-1, FR-2, FR-3) is in progress, as these are asynchronous operations.

5. Non-Functional Requirements (NFR)

ID	Requirement	Description
NFR-1	Performance	The maximum duration for the end-to-end execution of the full chain (FR-1 through FR-3) should not exceed 60 seconds.
NFR-2	Data Integrity	All intermediate and final results must be persistently stored (TR-5) and associated with the unique user session and ID.
NFR-3	Security	The application must use the Canvas-provided Firebase authentication tokens (__initial_auth_token) for secure data access to Firestore.

6. FR-1: Required JSON Schema

The output of the first LLM call (FR-1) must strictly adhere to this schema:

{
  "type": "OBJECT",
  "properties": {
    "innovationMethods": {
      "type": "ARRAY",
      "description": "A prioritized list of innovative thinking methods suggested for the problem statement.",
      "items": {
        "type": "STRING"
      }
    }
  },
  "required": [
    "innovationMethods"
  ]
}

This requirements list provides a strong foundation for building a robust and predictable Gen-AI application. Let me know if you'd like me to start prototyping the front-end interface in a React or HTML file!

I have created the detailed requirements document, requirements_document.md, which defines the prompt chaining architecture, data flow, and functional specifications for your application. This document includes the required JSON schema for the first LLM step.

Do you want me to proceed with generating a single-file application (HTML or React) to implement this structured prompt chain?

From <https://gemini.google.com/app/>  Google Gemini 2.5 Pro