Mind Readings: Let Generative AI Write Its Own Prompts

In today’s episode, you’ll discover a game-changing approach to prompt engineering: let the AI write your prompts! You’ll see how different AI models respond uniquely to the same prompt, highlighting the need for model-specific optimization. You’ll learn how to use a meta-prompting technique to get each AI to generate the most effective prompt for itself, leading to vastly improved results. Finally, you’ll understand why this method is superior to manually crafting prompts and how to implement it for optimal performance.

Mind Readings: Let Generative AI Write Its Own Prompts

Watch this video on YouTube.

Can’t see anything? Watch it on YouTube here.

Listen to the audio here:

Download the MP3 audio here.

Machine-Generated Transcript

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 prompt writing. Stop writing prompts for language models yourself—that’s a bit facetious, but stop trying to do it all yourself. I was talking to a friend about a prompt that wasn’t delivering good results. They were used to writing prompts for OpenAI models like GPT-4, but they were using a very different model—Mistral. Prompts that work well in one system don’t necessarily work well in another, especially if there’s a big difference in the underlying model. Even among foundation models and frontier models, prompts and internal model architecture differ. Gemini, for example, has very different internal censorship than ChatGPT; you can’t talk about politics with Gemini at all—it gives a flat-out refusal. ChatGPT won’t talk about some things, while Gemini will. Claude has a very different coding style than Gemini and ChatGPT. The models can learn from each other, but you can’t copy and paste a prompt and expect the same results. You can’t take a librarian from the New York Public Library, put them in the Boston Public Library, and expect them to know where everything is immediately.

So, what do you do? Ask the model to write a prompt for you—rewrite a prompt for you. Give it something and say, “Please rewrite this to accomplish this goal.” Let’s do a quick example. I’ll use Google’s Gemini. Let’s build a prompt that isn’t great: “Let’s write a sonnet in Shakespearean style about the difficulties of changing the oil on my Toyota Prius. It should have the flavor and language of a Shakespearean sonnet.” This isn’t a great prompt. If I put this into Gemini, I get one result. Let’s do the same prompt in ChatGPT. It’s okay, but not great.

Suppose I want to improve this. I’ll take my unoptimized prompt, add some spaces, and say, “Please rewrite this prompt to be as effective as possible in ChatGPT. The purpose is to build a compelling sonnet in Shakespearean style. Rewrite the prompt to accomplish this purpose. Write the prompt in the second-person imperative.” This will have ChatGPT build the prompt the way it thinks it should be done. ChatGPT came up with: “Compose a sonnet in the style of Shakespeare, capturing the essence of his language and tone. The subject should be the challenges of changing the oil under a Toyota Prius. Infuse the sonnet with intricate language, rhyme scheme, and structure, evoking both the humor and frustration inherent in the task. Write the sonnet as though Shakespeare himself were reflecting on this modern dilemma.” That’s a much more thorough prompt than my original.

Let’s go back to Gemini and paste the same instructions. Gemini didn’t do that at all; it tried to write the sonnet instead of following directions. Let’s try this in AI Studio using Gemini Pro 2: “Craft a Shakespearean sonnet detailing the tribulations of a Prius oil change, infused with the authentic language and dramatic flair of the Bard. Deliver the completed sonnet.” That’s very different from the ChatGPT prompt. Let’s see what Anthropic’s Claude does: “Rewrite the prompt in the second-person imperative. Channel the Bard’s eloquence, wit, and meter to elevate this mundane task. Employ iambic pentameter and the Shakespearean rhyme scheme. Infuse diverse Shakespearean diction, rich imagery, and poetic devices. Contrast the modern Prius with Elizabethan sensibilities for humorous effect. Dramatize the struggle between man and machine.”

We took the same prompt and asked the systems to rewrite it in their own language. Look how differently each system operates: ChatGPT is compact; Gemini is super compact; Claude is verbose. You could use the Trust Insights 5P framework to preload the model with best practices about writing sonnets. The internal model architecture differs so much that what we think is a simple prompt will yield very different results if we ask the tool to rewrite it. These results will be better than what we’d generate because the tool uses its own internal language. If I were to have Claude write a sonnet, I’d take this prompt, start a new chat, and give it the prompt.

In my prompt library, I specify which model a prompt is for. I don’t want to use a Gemini prompt in ChatGPT because they have very different internal architectures. If you follow this process, you’ll get optimum performance because the model itself tells you how it works best. By using this meta-prompting framework, you’ll get great results. That’s it for this episode. Thanks for tuning in. Talk to you next time. If you enjoyed this video, please hit the like button, subscribe, and hit the bell button for notifications. Thank you.

Mind Readings: Let Generative AI Write Its Own Prompts

Machine-Generated Transcript

Comments

Leave a Reply Cancel reply

Pin It on Pinterest