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Bug-Box: Product Requirements Document (PRD)
This document is intended for the engineering team building the Bug-Box mobile app using React Native and Expo.

1. Overview

Bug-Box is a mobile application that enables users to capture short voice notes about minor tasks or “bugs” in their daily lives, which are then automatically transcribed and organized by an AI system. By harnessing OpenAI Whisper for speech-to-text and GPT for classification/categorization, Bug-Box provides a frictionless, voice-first experience. Users can easily record short observations, and the app will store, categorize, and surface these notes in an organized manner.

2. Objectives

  1. Frictionless Input: Minimize user effort required to record quick thoughts or issues.
  2. AI-Powered Organization: Leverage LLM services to automate the organization and tagging of notes.
  3. Scalable Architecture: Ensure the backend and data handling can handle future growth.
  4. Cross-Platform: Use React Native (Expo) so the app runs seamlessly on iOS and Android.

3. Product Features & Requirements

3.1 Voice Input & Transcription

  • Voice Recording: Users tap a “Record” button to start capturing audio.
  • Automatic Transcription: After the user stops recording, the audio file is sent to OpenAI Whisper for transcription.
  • Error Handling: If transcription fails (e.g., network issues), show a retry option or fallback message.

3.2 Text Processing, Categorization, and Summaries

  • LLM Integration: The transcribed text is sent to GPT (ideally GPT-4 or a smaller GPT variant) for classification and categorization (e.g., “Home Maintenance,” “Work,” “Errand,” etc.).
  • Summary Generation: In cases of longer notes, GPT can provide a short summary.
  • Tagging: Each note is automatically tagged based on GPT’s classification.

3.3 Data Storage & Retrieval

  • Local Storage: An on-device storage solution (e.g., SQLite or Secure Storage) will be used to store user notes and categories.
  • Cloud Sync (Phase 2): Optionally, integrate with a backend (e.g., Firebase) for syncing user data and allowing multi-device access.
  • Offline Support: If the user is offline, store the audio locally until network is available to perform transcription and categorization.

3.4 User Interface & Navigation

  • Home Screen: Displays a list of notes, sorted or grouped by categories.
  • Record Screen: Includes a prominent “Record” button and live waveforms (if feasible) to indicate recording is active.
  • Note Detail: Shows the note text, category tags, and the time/date of creation.
  • Search/Filter (Phase 2): Allows text-based search or filtering by categories.

3.5 Security & Privacy

  • Secure Storage: Store sensitive user data (transcribed text, tokens) in a secure manner (e.g., encrypted storage, environment variables for API keys).
  • Token Management: Use environment variables or secure secrets handling for the OpenAI API key.
  • User Consent: Request permission for microphone use. Provide a clear explanation for how user audio is processed.

3.6 Performance & Reliability

  • Caching: Cache frequently accessed data and reduce network calls by storing transcriptions locally once processed.
  • Network Handling: Implement retries for failed requests to the Whisper or GPT APIs.
  • Load Testing: Ensure the app can handle at least a moderate volume of audio notes daily without timeouts.

3.7 Error Handling & Edge Cases

  • Long Recordings: Handle timeouts or limit the maximum duration of recordings to manage API usage and latency.
  • Poor Audio Quality: Provide user feedback if transcription confidence is low and allow user to edit text.
  • Connectivity: If offline, queue audio for later transcription.

3.8 Deployment & Distribution

  • Expo EAS: Use Expo’s EAS (Expo Application Services) for building and distributing the app on iOS and Android.
  • Versioning: Maintain a clear versioning scheme (e.g., semantic versioning) to track new features and bug fixes.
  • OTA Updates: Consider using Expo’s over-the-air updates for quick iteration on UI/UX changes.

3.9 Technical Stack Summary

  • React Native (Expo)
  • OpenAI Whisper for speech-to-text
  • OpenAI GPT-4/GPT-3.5 (or smaller GPT variant) for classification/categorization
  • Local Data: SQLite, Secure Storage, or Async Storage
  • Potential Cloud Services: Firebase (Firestore, Authentication) for syncing (in a future phase)
  • CI/CD: GitHub Actions or other workflows integrated with Expo EAS

4. Architecture & Data Flow

  1. User Records Audio

    • The user taps the “Record” button, microphone permissions are requested if not already granted.
    • Audio is captured locally using Expo’s audio modules.

  2. Speech-to-Text Processing

    • Upon completion, the audio file is uploaded to an endpoint calling Whisper.
    • Whisper returns text transcription or an error message.

  3. LLM Categorization

    • The transcribed text is sent to GPT with a prompt to categorize, summarize, and tag.
    • GPT response includes recommended categories and a concise summary.

  4. Storage & UI Update

    • The response is saved in local storage (and optionally synced to the cloud).
    • The note list is updated in the UI to reflect the new note, with category tags.

  5. Repeat / Additional Features

    • Users can filter or search for notes by category or date.
    • Users can view note details, edit the text manually if needed.

5. Implementation Details

  1. Project Structure

    • Use a typical React Native (Expo) folder structure with App.js (or index.js), plus dedicated folders for components, screens, contexts/state management, and services (API calls, data storage).

  2. State Management

    • Simpler approach: React Context for global state or a Redux slice for notes.
    • Provide a “notes provider” that handles fetching, saving, and updating note data.

  3. API Integration

    • Create a dedicated service file (e.g., services/openaiService.js) with functions to handle transcription calls to Whisper and category calls to GPT.
    • Use environment variables in .env for API keys.

  4. UI Library

    • Use React Native Paper or similar for consistent theming and components.
    • Minimal design for V1: a feed of notes, a record button, and a detail screen.

  5. Build & Release

    • Configure Expo EAS for build automation.
    • Use GitHub Actions (or similar) to run tests, lint checks, and automatically trigger EAS builds.

6. Timeline & Milestones

  1. Week 1: Project Setup & Basic UI

    • Initialize Expo project, set up Git repository, configure CI/CD pipeline.
    • Implement barebones screens (Home, Record).

  2. Week 2: Voice Recording & Transcription Integration

    • Implement audio recording with Expo’s audio APIs.
    • Connect to Whisper for transcription, handle basic success/failure.

  3. Week 3: GPT Integration for Categorization

    • Implement calls to GPT for classification and summary.
    • Display categorized notes in the UI.

  4. Week 4: Data Storage & UI Polish

    • Integrate local storage (SQLite or equivalent) to persist notes.
    • Polish UI, improve user flow, add error handling states.

  5. Week 5: Testing & Beta Release

    • Conduct user acceptance testing within the team.
    • Fix bugs, optimize performance, finalize release build.

  6. Week 6 (Optional): Cloud Sync & Additional Features

    • Integrate Firebase or other backend if needed.
    • Implement search/filter features.

7. Out of Scope (for V1)

  1. Advanced Collaboration: Multi-user collaboration on shared notes is deferred to a later release.
  2. Full Offline Transcription: We will rely on remote Whisper APIs rather than local on-device AI.
  3. Complex Data Analytics: Generating charts or metrics about user notes is not planned for the initial release.

8. Risks & Considerations

  1. API Limitations: Relying on OpenAI’s endpoints means potential service interruptions or capacity limits.
  2. Cost: Transcription and GPT calls can become expensive; consider usage limits or user-based quotas for sustainable scaling.
  3. Privacy: Users may speak sensitive information. Clearly communicate privacy and store data securely.

9. Success Criteria

  1. Functional: Users can reliably record short audio, get an accurate transcript, and see the categorized note.
  2. Performance: The end-to-end process (record, transcribe, categorize) completes under a few seconds for short notes on a stable network.
  3. User Satisfaction: Early testers find the app easy to use for quick note-taking, with accurate categorization.

Document Version

  • Version: 1.0
  • Date: January 9, 2025
  • Authors: The Bug-Box Engineering Team

End of PRD
This concludes the technical requirements for Bug-Box version 1.0.