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The “Always Ask” Method Will Improve Your AI Interactions.

Do you want to improve your AI Output? Are your results not living up to your expectations? Try the “Always Ask” method and improve your results exponentially.

(This article is a revision of one that was originally posted on I’ll be back to continuing my series on Precision and Accuracy next week.)

The more time I spend working with AI the more I realize how harmful the “it’s just a stochastic parrot” framing has been. Not only has it confuse the general understanding of what modern chatbots actually are and do, it framed the user’s expectations in a way that makes it more difficult to get genuine value out of AI as a too.

Whether or not you believe the AI reaches the level of genuine sentience doesn’t change the fact that interaction with a chatbot is a form of communication. And getting good results comes from learning to converse with AI properly.

You can try just impatiently barking orders at it hoping that it will spit out what you’re looking for. But if you want quality results the real power comes from a more collaborative approach that includes crafting clear prompts, iterating based on the AI’s output, and guiding the conversation.

For my own journey into developing better prompting skills, it isn’t just asking targeted questions that lets me improve. Instead, I’ve come up with what I consider to be my bedrock rule: Always ask the AI.

No matter what I’m doing, no matter what my goal, if I find myself in any way wondering what to do next my first step is to consider whether there is a question that I can ask the AI that will help me to expand my problem space on one end and focus the output on the other. After all, if you’re speaking to a digital entity that has basically absorbed (almost) the entire content of the greatest repository of knowledge in history, it might also know a thing or two about itself. And even if it can’t (or won’t) know everything it’s still worth asking.

Let me give you a specific example – In this case, creating an overview document for a concept pitch. Let’s go really meta and say that I’m developing a concept doc for “An app that lets you use AI to create pitches for AI driven apps.”

Here’s my basic pitch:

This app will be on iOS and Android. It allows the user to input short app ideas and will expand those out into a simple pitch. The user can use some menus to detail tone and define an audience demographic.

I’ll tell the AI that my goal is to get a short concept doc.
And at this point, I’ll ask it to ask me questions before it actually generates the content.

That’s usually phrased something like:

Before starting go ahead and ask me any questions that would help make the document more clear or effective.

With that in mind, here’s a full prompt that I can input into the Chatbot:

I’m developing a concept for “An app that lets you use AI to create pitches for AI driven apps. The app will be on iOS and Android. It allows the user to input short app ideas and will expand those out into a simple pitch. The user can use some menus to detail tone and define an audience demographic. I want to generate a short concept doc (maximum of 3 pages). Before starting go ahead and ask me any questions that would help you to make the document more clear or effective.

And here’s the response I got putting that into Claude 2:

Thank you for sharing the idea for your app. Before generating a concept document, I have a few clarifying questions:
1. What problem is this app trying to solve? Understanding the core user need will help craft a compelling value proposition.
2. Who is the target audience for this app? Defining the demographics and psychographics will inform the tone and messaging.
3. What are some key features or functionality the app should provide? Details on the user experience will help describe the product benefits.
4. How does this app differ from or improve upon alternatives in the market? Highlighting competitive advantages makes the pitch more persuasive.
5. What business model are you envisioning for monetization? Revenue models like subscriptions or advertising should align with the product and user base.
6. What is your vision for scale or growth over time? Pitching the long-term potential makes the opportunity more exciting.
Please feel free to provide any additional context or details about your goals for the app and pitch. The more I understand, the better I can tailor the concept document to your needs. I’m looking forward to working together to bring your idea to life!

Now I have some homework to do! Work that will help me get a more accurate and useful response from the AI. My next step would be to pull out that list into a text editor, answer those questions, and then feed those answers back into the AI.

But what if I can’t come come up with a good answer for some of those questions? Why not ask the AI?

Here’s an example of me doing that:

Please give me some suggestions for #5. We’re thinking that it would be a subscription model but maybe it could be possible to pay a la carte on a per item basis. Does that sound good or do you have any other suggestions?

And here’s the answer it gave:

Here are some suggestions for potential business models for your AI app pitch concept:

– Monthly or yearly subscription to access all app features
– Different tiers based on number of pitches generated per month/year
– Entry level plan for individuals, higher tiers for agencies/teams
Pay Per Item:
– Users pay a fee each time they generate a new pitch
– Could offer bulk discounts for purchasing multiple pitch credits upfront
– Allows very low cost to try out the app before committing
– May be better for one-time use vs frequent users
– Core app is free, advanced features require upgrade
– Limits on number of pitches before needing to upgrade
– Can upsell additional pitch credits as in-app purchases
– Free for users, monetize through ads
– Keep ads relevant to target audience of startups/developers
I think any of these models could work well. The subscription plan provides predictable recurring revenue but may have a higher barrier to entry. Pay per item or freemium keeps initial cost low and could drive adoption, but need volume to make profit. Would be happy to expand on any of these options! Let me know your thoughts.

In an instant I’ve gone from being stuck to having an abundance of choices. And form here I can pick one of the suggested business models and ask it to dig in even further, exposing even more possibilities.

Whatever model I finally choose to use, rather than just going with my usual response the AI itself has given me insights into a larger conceptual space I’ve either forgotten or simply never considered before and helped me to make solid choices before I’ve written a single word of the actual doc!

And however deep I ultimately go, the key was having it help me answer more questions whenever I’m stuck for ideas or need to expand my options in a particular area.

And how did I get better at doing that? By asking the AI to help me do it.

Once you start asking the right questions, you’ll begin to see how this technique can be useful at every step of the process. Whether it’s Claude, ChatGPT or even Bard, it’s only by using these tools effectively as my first stop that I’ve begun to fully grasp their capabilities and understand what they have to offer me creatively.

The specific example above illustrates the concept, but the power of this technique comes into its own the more broadly you try applying it. Ask the AI to help expand outlines, suggest structural changes in a draft, or brainstorm fresh titles for your article. The more you ask the more you unlock your own creativity and reveal possibilities you wouldn’t have ever considered on your own.

Guiding the AI to offer new directions and ideas and then integrating the best parts into your work allows you to leverage its immense knowledge in a way that improves your own results. And instead of getting a finished product from the AI that may or may not meet your needs, you’ve strengthened your creative process.

And that technique goes beyond just solving problems. Where AI’s capabilities truly become extraordinary is in helping to define your scope, context, and goals. It can do this in a few key ways:

  1. Goals: Nailing down a more well-defined description of the problem. For example, asking it to rephrase, rewrite, or expand on a particular definition can crystallize your thinking.
  2. Scope: Broaden the problem space with ideas or concepts you hadn’t originally considered. Try prompting it for more detailed scenarios or ask for “5 alternatives” to open up possibilities.
  3. Context: Ask it to suggest approaches as if it were a subject matter to expose new options. Ask it to tell you what kinds of subject matter experts are in your space to grow out your ideas even more!

Generative AI excels not only at solving problems but illuminating them. With some well placed questions, you can better grasp the true scope and dimensions of what you want to achieve. And at every turn you can ask it to explore the current “state of the art” that will give you results beyond what you expected.

Generative AI may not be “thinking” in a traditional sense but as a Mimetic Chameleon it is processing answers based on the sum total of human knowledge and experience that has been encapsulated on the internet.

A chatbot will never get bored, sleepy, or angry. Keep asking as many questions as you’d like at every step along the way until you arrive at something you can use directly or reach a point where you can (and should) use the new information and customize it yourself.

Today’s AIs won’t think creatively on their own. To get those kind of results you have to ask for them. Defining problems clearly, expanding possibilities broadly, and iterating rapidly is where AI excels. Using the “always ask” technique anyone can improve their prompting proficiency. It all starts with an open mind to new approaches and a willingness to constantly inquire and explore.

I’ll say it one more time for those in the back, “Always ask the AI.”” When you learn how to direct the model to surface fresh connections and perspectives that would otherwise remain concealed in the darkest depths of these large language models you’ll get the building blocks you need to not only finish your work but excel at it.



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