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In-Ear Insights: The Trust Insights RAPPEL AI Prompt Framework Explained
In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the new RAPPEL AI prompt framework for AI prompting, designed to simplify and improve your interactions with AI. Discover why priming the model is crucial for accurate results and how this framework helps you avoid common pitfalls. Learn how RAPPEL streamlines the process of creating effective prompts and how to use it to scale your use of AI. Unlock the secret to repeatable AI success by learning from each interaction and building better prompts for future use.
Watch the video here:
Can’t see anything? Watch it on YouTube here.
Listen to the audio here:
https://traffic.libsyn.com/inearinsights/tipodcast-trust-insights-rappel-ai-prompt-framework.mp3
Download the MP3 audio here.
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Machine-Generated Transcript
What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.
Christopher S. Penn – 00:00
In this week’s In Ear Insights, the RACE is over—as I was jokingly saying last week with our change in our AI framework. So, previously we had two frameworks: the RACE framework and the PARE framework. We have since upgraded and changed this to what we now call the RAPPEL framework. So Katie, before we dive into what the thing is, you’ve had a chance to at least look at it. What’s your first impression of this? Is it a good idea to throw out something that’s been working for people for two years now?
Katie Robbert – 00:37
That’s a loaded question. I was actually thinking about this over the weekend in terms of frameworks like SWOT, for example. Strengths, weaknesses, opportunities, threats. There’s a reason that framework has stayed tried and true for so long and unchanged because it works. It’s easy to understand. Now, when it comes to AI, it’s changing so rapidly that I think you do have to adapt the frameworks. I think we’re talking about two things. If I use the example of SWOT, you’re talking at that high-level overview versus talking about frameworks like RACE and PARE, and now RAPPEL—you’re talking more tactical and on the ground.
I feel like those frameworks that are more on the ground, that get into the weeds, are the ones that have to continue to adapt. Now, should we continue to change the actual instructions of the framework over and over again? At some point, we should probably say this is going to work regardless of how AI evolves. But I think because AI is still, for all intents and purposes, in its infancy in the consumer space, continuing to adapt as we learn more is not a bad idea. That said, we’re at six steps for a framework. If we start to get beyond that, it’s when I think it’s going to be harder and harder for people to continue to follow along and adopt this framework.
The purpose of a framework is to keep things very consistent, easy, and not difficult to remember the steps. So, I think you’re at that tipping po
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By Trust Insights
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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the new RAPPEL AI prompt framework for AI prompting, designed to simplify and improve your interactions with AI. Discover why priming the model is crucial for accurate results and how this framework helps you avoid common pitfalls. Learn how RAPPEL streamlines the process of creating effective prompts and how to use it to scale your use of AI. Unlock the secret to repeatable AI success by learning from each interaction and building better prompts for future use.
Watch the video here:
Can’t see anything? Watch it on YouTube here.
Listen to the audio here:
https://traffic.libsyn.com/inearinsights/tipodcast-trust-insights-rappel-ai-prompt-framework.mp3
Download the MP3 audio here.
[podcastsponsor]
Machine-Generated Transcript
What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.
Christopher S. Penn – 00:00
In this week’s In Ear Insights, the RACE is over—as I was jokingly saying last week with our change in our AI framework. So, previously we had two frameworks: the RACE framework and the PARE framework. We have since upgraded and changed this to what we now call the RAPPEL framework. So Katie, before we dive into what the thing is, you’ve had a chance to at least look at it. What’s your first impression of this? Is it a good idea to throw out something that’s been working for people for two years now?
Katie Robbert – 00:37
That’s a loaded question. I was actually thinking about this over the weekend in terms of frameworks like SWOT, for example. Strengths, weaknesses, opportunities, threats. There’s a reason that framework has stayed tried and true for so long and unchanged because it works. It’s easy to understand. Now, when it comes to AI, it’s changing so rapidly that I think you do have to adapt the frameworks. I think we’re talking about two things. If I use the example of SWOT, you’re talking at that high-level overview versus talking about frameworks like RACE and PARE, and now RAPPEL—you’re talking more tactical and on the ground.
I feel like those frameworks that are more on the ground, that get into the weeds, are the ones that have to continue to adapt. Now, should we continue to change the actual instructions of the framework over and over again? At some point, we should probably say this is going to work regardless of how AI evolves. But I think because AI is still, for all intents and purposes, in its infancy in the consumer space, continuing to adapt as we learn more is not a bad idea. That said, we’re at six steps for a framework. If we start to get beyond that, it’s when I think it’s going to be harder and harder for people to continue to follow along and adopt this framework.
The purpose of a framework is to keep things very consistent, easy, and not difficult to remember the steps. So, I think you’re at that tipping po
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