When I run AI workshops the room is generally split between those who “get” AI and those who don’t.

Some people tried AI for the first time and it clicked immediately. They got solid results. They continued using it. They skilled up, kept up to date. They have become power users and can’t imagine life without AI.

The others maybe gave AI a shot, didn’t get the results they expected and…gave up.

They are the ones who are likely to say “eh, AI is overhyped. It’s a bubble” and the like.

They are the ones who will fall behind.

When working with these two groups I’ll have them run a basic prompting exercise. So I get to see the sort of prompts that they are using with AI.

Without fail those who don’t think much of AI are using crappy prompts. Think “write me some good marketing copy” level of crappy prompts.

The difference between these two groups is not technical knowledge. The difference between the two groups - those who get good results and those who get bad results from AI - is how they prompt.

Prompting is, at its base, a communication skill. And as a skill it’s something we can refine and practice.

Some people naturally speak AI's language; others need to learn it. And all of us, without exception, can get better!

And that's exactly what we're focusing on in this Playbook.

Let’s get started

The Communication Divide

In the early days of personal computing, people spoke about being "computer literate" – having the basic skills to operate these new machines. Today, we're facing a similar divide with AI, but it's not about technical skills – it's about communication.

Some people naturally click with AI tools. They instinctively understand how to communicate their intentions clearly, frame their requests effectively, and iterate when necessary.

Others struggle, finding themselves frustrated by inconsistent or off-target results.

If you are in the first group you might sometimes find yourself wondering why other people find it so difficult. Are they just stupid?

This divide is not about intelligence or technical knowledge. I've seen brilliant developers struggle with prompting while non-technical folks get magnificent results. It's specifically about communication style and clarity.

Great news for those who are less technical. This new paradigm of working with computers opens up opportunities for communicators.

And good news for everyone: unlike coding or advanced technical skills, effective AI communication can be learned quickly. It's a skill that builds on capabilities many of us already have – we just need to adapt them for this new context.

The RISEN™ Framework: A Starting Point

To help bridge this communication gap, I've developed the RISEN™ framework – a straightforward approach to structuring your prompts:

R - Role: Establish who or what the AI should be. I - Instructions: Provide clear, specific directions. S - Steps: Break complex tasks into manageable pieces. E - End Goal: Clarify what success looks like. N - Narrowing: Add constraints to refine the output.

Here’s a video detailing the framework

View referenced TikTok post

This framework isn't the only way to communicate effectively with AI, and you won't always need to follow it rigidly. Think of it as training wheels – a structure that helps build good habits until clear communication becomes second nature.

Let's briefly explore each element

ROLE: Setting the Context

Starting your prompt by assigning a specific role to the AI immediately establishes context and perspective. It's like telling a colleague, "Put on your marketing hat for a moment" or "Think about this from an engineer's perspective."

For example, rather than asking: "How do I improve my sales pitch?"

Try: "As an experienced sales director who has closed deals with Fortune 500 companies, help me improve my sales pitch."

The role you assign shapes how the AI approaches your request, often leading to more tailored, relevant responses. It’s useful because AI can wear any hat which means it tends toward generic responses. By specifying what role we want it to take on right now we can refine answers.

INSTRUCTIONS: Crystal Clear Directions

After establishing the role, provide explicit instructions about what you want. The more specific, the better.

Poor instructions are vague: "Give me content ideas."

Better instructions are specific: "Generate 5 LinkedIn post ideas about artificial intelligence for small business owners, each with a surprising statistic or insight that highlights the practical benefits rather than the technical details."

The second example gives the AI clear guidelines about quantity, topic, audience, content type, and emphasis – all crucial elements for generating useful output.

STEPS: Breaking Down Complexity

For complex requests, breaking the task into sequential steps dramatically improves results. This is particularly effective for analytical tasks, writing assignments, or any request that requires structured thinking.

Instead of: "Analyze this marketing data and give me recommendations."

Try: "Analyze this marketing data by

1. Identifying the top 3 performing channels by ROI

2. Highlighting unexpected trends or anomalies

3. Comparing this quarter's performance to the previous quarter

4. Recommending 3 specific actions based on these insights"

The best way to think about this is as if you were providing a complex task to a (human!) assistant. Sure, you know how to complete a particular task but maybe your assistant doesn’t - at least the first time. Because of this we break down the task for them. We’ll do the same with AI.

END GOAL: Defining Success

Always clearly state what you're trying to accomplish. What's the ultimate purpose of this prompt? What problem are you solving? What does success look like?

So many people get annoyed with poor results from an AI but at no point did they actually specify what success looks like… so, how is it to know??

A good prompt example might look like this: "The goal is to create an email template that increases our webinar registration rate by addressing our audience's pain points and creating urgency without sounding pushy."

Specifying your end goal helps the AI prioritise the most relevant information and approach the task with your desired outcome in mind.

NARROWING: Refining Through Constraints

After you get an initial response, the magic happens in the narrowing phase. This is where you add constraints, preferences, or additional context to refine the output.

For instance, if you receive a sales email that's too formal: "This is good, but please make it more conversational and friendly. Use shorter sentences, avoid jargon, and add a touch of humour that would appeal to creative professionals."

Narrowing is an iterative process that helps the AI better understand your expectations and preferences.

We can also keep a record of these narrowing constraints that we’ll use in similar prompts. Or indeed we build them into reusable prompts.

Beyond Frameworks

While the RISEN™ framework provides a helpful structure, the fundamental principle is clear communication. That’s all.

It’s not the only or best framework. And as you become more experienced, you may develop your own approaches or simplify your prompts while maintaining clarity.

The ultimate goal isn't to rigidly follow a framework but to communicate effectively with AI systems. It’s just a scaffolding to get you used to thinking about prompting (read: communication) more systematically.

Some situations might call for elaborate prompts; others might need just a few well-chosen words. Your ability to judge what's needed in each context will improve with practice.

Remember: even the most advanced AI systems can't read your mind. They can only work with the information you provide. The clearer and more complete your communication, the better your results will be.

Common Communication Barriers

Several patterns consistently undermine effective AI communication

Assuming Mind-Reading Abilities: Many users unconsciously expect AI to fill in contextual gaps that seem "obvious" to humans. Remember, AI has no access to your prior knowledge or intentions unless you explicitly state them. To fix this think about what information you would provide to a human assistant and provide the same level of context to an AI.

Overcorrecting with Extreme Detail: Some users swing to the opposite extreme, creating prompts so long and convoluted that key instructions get buried. Clarity doesn't always mean verbosity. This is likely to happen when you start uploading loads of documents and supplemental material - all of that hides the actual core instructions.

Using Ambiguous Language: Phrases like "make this good" or "improve this" are too subjective to be actionable. Be specific about what "good" or "improved" means in your context. This is straight up communication failure - be precise!

Neglecting to Iterate: Effective AI communication is often a dialogue, not a one-shot interaction. Be prepared to refine your request based on initial responses. We’ll talk more about different styles of prompts and how to “cement” them into workflows over this Playbook.

Recognising these patterns in your own interactions is the first step toward more effective AI communication. Hopefully at least one of these pops out as something you do! If so…stop it! 😆 

What's Next in Our Prompting Mastery Journey

This Part was a gentle appetiser! In this Playbook we're building a comprehensive toolkit for effective AI communication. We just focused on foundational principles and we'll progressively explore more advanced techniques:

Part 2- We'll dive into specific prompting techniques including zero-shot, one-shot, and few-shot approaches, as well as system and role prompting strategies. You'll learn when to use each technique and how to manage AI's tendency toward agreeableness.

Part 3 - We'll explore output control and system design, covering temperature settings, API vs. GUI interfaces, and how to experiment with different input and output formats.

Part 4 - We'll compare different AI models and discuss how to select the right one for specific tasks, while also covering strategies for adapting to model updates.

Part 5 - We'll conclude with advanced reasoning techniques and best practices, including Chain of Thought, ReAct, and strategies for working with reasoning models.

Keep Prompting,

Kyle