Build an AI Email Assistant that Learns Your Style

Learn how to develop a personal email assistant with AI using a single prompt.

January 02, 2025

Imagine an AI that replies to your emails, not just generically, but in your style. Today’s prompt explores how to develop a program to do just that by integrating Gmail and AI in one go.

Today’s Prompt: Build an AI Email Assistant that Learns Your Style

I am a developer working on a program that connects to a user's Gmail account, analyzes the last 25 emails they've replied to, and generates a guide based on their unique email response style. This guide will then be integrated into system instructions for an API call to a Gemini-like LLM, enabling it to generate email replies in the user's style. The program should feature a simple UI for users to easily connect their Gmail accounts, view their email reply style guide, and generate personalized email responses.

Here are the specific requirements for the program:

  1. Gmail Integration:

    • Authenticate users via OAuth 2.0 to securely connect to their Gmail accounts.

    • Access and read the last 25 email threads where the user replied, extracting both their replies and the original emails.

  2. Data Analysis:

    • Analyze the user's replies to identify patterns, tone, structure, word choice, and other stylistic elements.

    • Summarize the findings into a clear, concise "response style guide" (e.g., typical tone, preferred sign-off, frequency of emojis, formality level).

  3. Gemini API Integration:

    • Feed the "response style guide" into system instructions for an API call to Gemini or another LLM.

    • Provide prompts to the LLM for generating email replies based on the user's identified style and the context of the incoming email.

  4. Simple UI:

    • Design a user interface that allows users to:

      • Authenticate their Gmail account.

      • View their generated "response style guide."

      • Input a received email to generate a personalized reply using the LLM.

  5. Security:

    • Ensure all sensitive data (e.g., email content, OAuth tokens) is handled securely and complies with privacy standards like GDPR or CCPA.

Please provide the code to implement this program, including:

  • A backend service (e.g., Python Flask or Node.js) for Gmail integration, data analysis, and API calls to Gemini.

  • A frontend UI (e.g., React or Vue.js) for user interactions.

  • An explanation of the main components and how they work together.

  • Best practices for securing user data in this context.

Include comments in the code for clarity and modularity, and ensure it is scalable for future enhancements like analyzing more emails or supporting additional email providers.Why we like this prompt:

  1. Clear and Comprehensive Objective: The prompt explicitly defines the task of developing a program that analyzes a user’s email reply style and integrates with a Gemini-like LLM, ensuring focus and a clear development roadmap.

  2. Step-by-Step Requirements: It breaks the task into well-defined components, such as Gmail integration, data analysis, Gemini API integration, UI design, and security, guiding the developer in creating a complete and cohesive system.

  3. Focus on Practical Implementation: By requesting code for the backend, frontend, and security best practices, the prompt ensures the solution is actionable and ready for deployment.

  4. Emphasis on Security: The specific mention of handling sensitive data securely and complying with privacy standards highlights the importance of protecting user data, a critical aspect of any app involving personal information.

  5. Scalability and Future-Proofing: By suggesting modular design and future enhancements (e.g., support for more emails or email providers), the prompt encourages the creation of a robust and adaptable system.

  6. Clarity in Collaboration: The inclusion of comments in the code ensures readability and facilitates future collaboration or maintenance, aligning with software development best practices.

Result:

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