In today’s episode, we delve into the concept of ‘prompt libraries’ in the realm of large language models and image modeling. I’ll share why it’s crucial for every AI user to keep a prompt library and how it can be leveraged to fine-tune AI tools like ChatGPT or DALL-E to align more with your unique needs. From explaining how to create a prompt library to its future implications, we journey through the interesting intersections of AI and our daily communication. Join me as we uncover the practicality and potential of this powerful asset.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today’s episode, let’s talk about prompts within the context of large language models and image modeling.
So generative AI, we’re talking about prompts in the sense of what you type into a tool like chat GPT or Dali or stable diffusion, or mid journey, any of these companies and their prompts.
First, if you are not already keeping a prompt library, you need to be if you’re using these tools, if you want to be able to use these tools more and use them in production and gain benefit from them, you should have a prompt library.
This is something I talked about in the the keynote speech they do on this topic.
What is a prompt library? It’s a notebook, right? It’s just a notebook, a filing system of some kind.
You can make it in, you know, Google Docs, you can make it in tools like Evernote or OneNote or Joplin, which is the tool that I use, obsidian scrivener, it doesn’t matter.
There’s so many tools for keeping notes as long as it’s a tool that you can find stuff in when you search for it.
And depending on your situation, your company, you may need other people to be able to find stuff in it.
That prompt library is where you store your prompts that you write for a tool like chat GPT or mid journey.
And if you can, you’ll store the outputs as well.
So let’s say you have a prompt, maybe it’s a it’s a YouTube caption prompt, which is one of the ones that I use all the time.
You say you’re an expert YouTube content manager, you know, YouTube, blah, blah, blah, blah.
The summary, you’re going to write a summary of this transcript, it should be no more than four sentences, write it in the first person, and so on and so forth.
And you would write that prompt use it.
And if it creates a satisfactory output, you stick it in your prompt library, say, okay, this is the one I use for YouTube captions.
This is the one I use to decline commercial pitches politely.
This is the one I use to write the summary for spy novel, whatever it is you’re using it for, you want this prompt library.
Now, there are very good people and process reasons for doing this again, keeps things organized helps you share it with others.
But here’s the part that people aren’t thinking about yet.
The future of a lot of these generative AI tools is in fine tuning.
And by fine tuning, we mean taking an existing model and tuning it, making it more like you through a process of gathering data, and putting that training data into the model and having the model change its responses based on the data it’s given.
For example, not too long ago, I took all the blog posts that my CEO and partner Katie Robert had written for the trust insights blog.
And we fed this into a the fine tuning library for open AI is GPT three model.
And when we’re done, we got out a model that we could use that sounded like Katie, it sounded much more like Katie than any prompt ever has.
Because it was taking her words and putting them in.
That fine tuning is super important for being able to customize these models to do specific tasks to have a certain tone of voice and things like that.
And the process of getting one of these models stood up is laborious, it is extensive, it is highly technical, and it requires good data.
Where do you get that good data, you could get it from your prompt library, right? If you’ve been writing really good prompts for generating YouTube captions, and maybe you’ve got 50 or 60 of these things stored up.
Now you’ve got 50 or 60 examples you can you can fine tune a model on to help it do that task better.
You write a newsletter.
Each week, you could write a prompt that for which that newsletter would be the answer.
And now you’ve got again, some fine tuning data, my friend and handily who writes a bi weekly newsletter, she would have 26 new prompts and responses every week that she could train a model on tune a model on so that it could sound more like her than any prompt, you know, normal prompt ever would.
So you can’t do this.
If you don’t have the data stored, you can’t make this a reality.
The information is doesn’t already exist.
And so as you’re writing prompts, store them, categorize them, catalog them, keep the responses when they’re good.
And you will have a strategic advantage of competitors who are not doing this over who are not thinking ahead who are not saying, hey, this data might actually be useful.
Remember, and this is something again, I say in the keynote, which I’ll put a link so you can watch it.
Everyone is a developer.
And in the world of large language models and generative AI, everyone is a developer as long as you can write.
And every word you write is an opportunity, it’s programming code.
So that’s today’s thought for you, your prompt library is your training data for a future fine tuned model.
Thanks for tuning in.
Talk to you next time.
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