Here's a long list of a random document. Explain core concepts and explain how they work in theory: Comprehensive Survey App Development Checklist
This checklist outlines the key steps and considerations for developing a fully functional survey app, incorporating features like survey type badges, AI capabilities, an Aero Frutiger UI, and advanced bot-checking mechanisms.
Phase 1: Planning & Design
1.1. Project Definition & Scope
[ ] Define Target Audience: Clearly identify who will be using the app (survey creators and survey takers).
[ ] Core Functionalities: List all essential app functions (user registration, survey creation, survey taking, results viewing, admin panel).
[ ] Specific Feature Deep Dive:
[ ] Survey Badges: Define types (e.g., "AI Conversation," "Classic Original," "External Link," "Quick Poll," "Feedback Form"). Specify visual style and placement.
[ ] AI Features: Brainstorm specific AI integrations (see section 1.4).
[ ] Aero Frutiger UI: Gather visual references and define key design principles (see section 1.3).
[ ] Bot Check (Biometrics & Passkey): Research platform capabilities and standards (see section 1.5).
[ ] Project Goals & KPIs: What does success look like (e.g., number of users, surveys created, engagement rate)?
[ ] Budget & Timeline: Establish realistic financial and time constraints.
[ ] Competitive Analysis: Analyze existing survey apps for strengths, weaknesses, and opportunities.
1.2. Feature Prioritization
[ ] Must-Have Features (MVP): Core survey functionalities, essential security, one or two badge types, basic AI feature (e.g., AI survey title suggestion), initial bot check.
[ ] Nice-to-Have Features: Advanced AI analytics, more badge types, gamification, complex survey logic.
[ ] Future Scope: Features for later versions.
1.3. UI/UX Design - Aero Frutiger Style
[ ] Research Aero Frutiger:
Study examples (Windows Aero, classic airport signage).
Key elements: translucency (glassmorphism/acrylic), subtle gradients, soft lighting effects, clean sans-serif typography (like Frutiger), emphasis on clarity and legibility.
Consider how to modernize it while retaining its core feel.
[ ] User Personas & Journey Maps: Understand user needs and interaction flows.
[ ] Wireframes: Low-fidelity sketches of all screens and user flows.
[ ] Mockups & Prototypes (High-Fidelity):
Visually design all screens incorporating Aero Frutiger aesthetics.
Design distinct icons/visuals for survey badges.
Create interactive prototypes for user testing.
[ ] Style Guide: Document colors, typography, iconography, component styles for consistency.
[ ] Accessibility (WCAG): Design for users with disabilities (color contrast, keyboard navigation, screen reader compatibility).
1.4. AI Feature Integration - Planning
[ ] Identify Specific AI Use Cases:
[ ] AI Survey Generation: User provides a topic, AI suggests questions, structure, and relevant badge.
[ ] AI Conversational Surveys: Surveys conducted through a chatbot-like interface.
[ ] AI Response Analysis: Sentiment analysis, keyword extraction, automated summaries of open-ended responses.
[ ] AI-Powered Insights: Identify trends or anomalies in survey data.
[ ] AI for Content Moderation: Flag inappropriate content in open-ended responses.
[ ] Choose AI Models/APIs: Research and select appropriate LLMs (e.g., Gemini API for text generation/analysis), NLP libraries.
[ ] Data Requirements: What data is needed to train/fine-tune models (if applicable)? How will user data be handled for AI processing?
[ ] Ethical AI Considerations: Plan for bias mitigation, transparency, and data privacy.
1.5. Bot Check System - Planning (Biometrics & Passkeys)
[ ] Research Biometric Authentication:
Platform-specific APIs (e.g., Face ID/Touch ID on iOS, Android BiometricPrompt).
Define user consent flow.
[ ] Research Passkey Implementation:
FIDO2/WebAuthn standards.
Server-side requirements for storing public keys and managing challenges.
[ ] User Flow: Design the sequence for bot verification during critical actions (e.g., registration, high-value survey submission).
[ ] Fallback Mechanisms: What happens if biometrics/passkeys are not supported, not set up, or fail? (e.g., CAPTCHA, email verification).
[ ] Security & Privacy: Ensure secure handling of any biometric identifiers (often managed by the OS) and passkey credentials.
1.6. Technology Stack Selection
[ ] Frontend: (e.g., React Native, Flutter for cross-platform; Swift/Kotlin for native; React/Vue/Angular for web).
[ ] Backend: (e.g., Node.js/Express, Python/Django/Flask, Ruby on Rails, Java/Spring Boot).
[ ] Database: (e.g., PostgreSQL, MySQL, MongoDB, Firestore) – consider needs for structured and potentially unstructured (AI conversation logs) data.
[ ] AI/ML Tools: Python libraries (TensorFlow, PyTorch, scikit-learn), cloud AI platforms (Google AI Platform, AWS SageMaker).
[ ] Cloud Platform: (e.g., AWS, Google Cloud, Azure) for hosting, database, AI services.
[ ] Version Control: (e.g., Git, GitHub/GitLab/Bitbucket).
1.7. Data Architecture & Security Planning
[ ] Database Schema Design: For users, surveys, questions (various types), responses, badges, AI metadata, passkey credentials.
[ ] Data Encryption: At rest (database encryption) and in transit (HTTPS/TLS).
[ ] Privacy Policy & Compliance: GDPR, CCPA, or other relevant regulations. Plan for data minimization and user consent.
[ ] API Security: Authentication (OAuth 2.0, JWT), authorization, rate limiting, input validation.