AI and Journaling: How ChatGPT can Revolutionize the Practice

Dan Shipper, from the beautiful publication “Every”, started using GPT-3 to journal as a personal development practice. Rather than staring at a blank page, he wanted journaling to feel more like a conversation. He would ask GPT-3 to pretend to be someone and then ask him questions to help unpack things in his life. More like a mashup of journaling and talking to a friend.

He started experimenting with prompts like this:

You are Socrates, please help me with an issue in my life. Please ask me questions to try to understand what my issue is and help me unpack it. You can start the conversation however you feel is best. Please end your responses with /e

Michelle Huang’s Twitter thread went viral when she pushed the idea further by feeding GPT-3 her journal entries so that the person she was talking to was a younger version of herself. 

Michelle asked her younger self what she thought about the world, what her values were, and what her perspective on things was, stuff that present Michelle was grappling with. Michelle even asked her younger self if she had questions. They talked about whether she had followed her dreams or not and if she was happy with her life at the moment. She was reminded of the parts of herself that stayed constant through the years, but also of the parts she forgot or buried as life went on.

The prompt she used to do this was:

The following is a conversation with Present Michelle (age [redacted]) and Young Michelle (age 14). Young Michelle has written the following journal entries: [diary entries here] Present Michelle: [type your question here]

One of the problems with this approach is the technical limitation on the amount of text GPT-3 can compute in one go. The latest models have a maximum limit of 4097 tokens, which is about 3000 words. If you include 2000 words of journal entries in your prompt, you can only have 1000 words of conversation before it stops working. Not ideal if you’ve been journaling for over 10 years.

Dan found a way around this memory problem by using a tool called GPTIndex.

GPTIndex can store 10 years of journal entries by breaking them down into smaller chunks, which are then indexed so they are easy to find and summarize. When you ask a question, the tool finds all relevant chunks and uses them as context for the conversation, effectively bypassing the 3000-word limit so you can work with as many journal entries as you want. You can read the full article, “Can GPT-3 Explain My Past and Tell My Future?” for more specifics on how he pulled this off.

And it gets stranger.

With the ability to feed GPT-3 years of journal entries, we effectively creating a “second brain” that we can talk to. But there’s nothing stopping us from creating a similar experience with someone else’s work. For example, Dan loves listening to the neuroscience podcast “The Huberman Lab”. Each episode is several hours long and it’s a hassle to scrub through hours of audio when he has a specific question in mind. So, he transcribed all the episodes. When he has a question, the bot finds the most relevant sections of the transcripts and sends them to GPT-3 with the following prompt.

Answer the question as truthfully as possible using the provided context, and if the answer is not contained within the text below, say "I don't know." [ relevant sections of Huberman Lab transcripts ] Q: What is task bracketing? A:

You can see the kind of pinpoint accuracy it comes back with in this demo on Twitter, or you can read about the who experience here, “I Built an AI Chatbot Base On My Favorite Podcast“

Dan repeated the process with Lenny Rachitsky’s entire archive of content. Lenny has been writing and talking about building software products and growth for years. Indexing his expertise and using it as context for questions allows GPT-3 to produce answers with specialized knowledge and nuance in a way that vanilla GPT-3 can’t match.

As Dan points out, this could give people the ability to monetize the content they’ve already created in new ways: “A new class of content creators will learn to create compelling chatbot experiences that combine their personality and worldview for their niche audience in the same way that some creators learned to create compelling YouTube videos, newsletter articles, or TikTok clips.”

We went from journaling to therapy, to building your own second brain, to creating what is probably going to become an entirely new form of media. And we’re still just talking about ingesting one person’s body of work here. Copyright complexities aside, what happens if I want to feed GPT-3 multiple bodies of work from people I admire and respect?

These are strange and exciting times.

I will continue to delve into this rabbit hole and share what I find.

If you want to create your own little chabot you will need someone with programming expertise to set it up for you. But you can use Michelle’s or Dan’s initial prompts to start journaling with ChatGPT.