I used AI to build a simple AI-powered tool without writing a single line of code
I'm always intrigued by innovations that can improve our development processes. Recently, I delved into the potential of OpenAI's GPT-4 for software development and code generation by building an email client prototype. The goal was to create a summary of unread emails in my inbox using the OpenAI API.
I started by asking the AI model to help me create user stories for the idea. The model came up with around a dozen high-quality stories. We selected three of them for our MVP and proceeded to generate code implementing the basic functionality. The AI provided perfectly working code that requested email server address, username, and password, and then generated a hardcoded example summary without fetching any emails.
The next step involved adding a function to connect to the email server and test access to my Gmail account. The AI model produced perfectly working code with instructions on integrating it into the existing script. We then added a function to communicate with the OpenAI API by sending a "hello gpt" message and returning a response. After obtaining the API key with the AI's assistance, I tested the code and noticed a gibberish response that looked like code. After some debugging and discussions with the AI, we realized that the code initially used an engine trained on code generation, which caused the gibberish response.
Interestingly, during our conversation about changing the engine, I asked the AI model to use GPT-4. It turned out that the GPT-4 model isn't aware of its existence because it's trained on older data (September 2021), before GPT-4 was released. However, the AI model didn't stop there – it went ahead and updated the code to use GPT-3 for better human-readable responses.
The AI model not only generated a function to fetch the last 10 emails in the desired format, but it also proactively suggested changes to the code to accommodate the email summaries. However, we encountered a scalability issue due to the model's 2000-token limit. To address this, the AI proposed summarizing each email individually and then generating a summary of summaries. The AI model was impressively proactive and generated the code without being explicitly asked.
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After some testing, we rewrote the email fetching function to auto-truncate emails if the body exceeded 2000 tokens. I manually adjusted the summary length and fine-tuned the prompt for the summary of summaries to achieve the desired result.
The final product is a working email client script that generates summaries of unread emails. Almost the entire code, GitHub repository description, and the first commit message were AI-generated, with just a few minor changes made by me. This whole article except this sentence is also generated by GPT-4 based on a brain dump that I threw at it after we were done with the code. You can check out the AI-generated source code here: https://github.com/svoloch413/gptemail/blob/main/email_client.py (Note: You'll need to create a file with your own API key and have an email server with IMAP enabled.)
The entire process, from idea to working script, took approximately 1 hour and 15 minutes, demonstrating the powerful capabilities of AI in software development.