In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris provide a practical guide to prompt engineering, prompt engineering 101. Learn how to approach prompt creation like software development, starting with thorough requirements gathering using the 5 P’s framework. Discover the power of the RACE framework (Role, Action, Context, Execute) for crafting effective prompts that produce high-quality outputs. Finally, understand the importance of incorporating feedback and refining your prompts over time to improve accuracy and efficiency.
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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 Penn 0:00
In this week’s In-Ear Insights, let’s talk about prompt engineering 101, but from a different perspective.
We’ve talked about prompt engineering a ton in terms of writing prompts and what it is, etcetera.
This week, as part of this four-part series, let’s talk about prompt engineering in the context of proper software development and the 101 of how you do that.
I pretty much assume we don’t need to talk about why prompt engineering is important.
ChatGPT has taken care of that for us, and we’ve talked about many of our different frameworks for doing it.
But to start off, Katie, when you’re talking about good software development—because prompt engineering is writing code, you are writing code, you just happen to be writing code in English, or in Danish, or Japanese, and not in Python, or Java, or C++—when you’re doing good software engineering, where do you start to ensure that you get a good result out of your software development exercises, whether it’s a little script or a big one?
Katie Robbert 1:09
Do you want to hear the shocking answer?
Christopher Penn 1:10
I’m sure I’m going to be stunned.
I’m going to guess it’s going to start with something like, I don’t know, requirements gathering?
Katie Robbert 1:19
You would be 100% correct.
Software development is one of those practices that people love to hate, and they hate to love, because developers—in broad strokes—developers don’t like to do documentation, they just want to write code, they just want to make things.
But good software development needs to have a set of requirements, and the requirements don’t have to be cumbersome.
I often talk about the company that I used to work at.
When we did requirements gathering, they were these long, 20-page documents detailing out every single piece down to the tiniest little detail, and it felt—the developers felt very micromanaged, like they had no flexibility to inject any of their own ideas because the stakeholders had written out, you know, spent weeks, months sometimes, on the set of requirements, and they were just asked to execute it exactly.
Now, the upside to that is that the stakeholders knew exactly what they were going to get.
The downside to that is that we had angry, resentful developers who felt like the solutions that they were proposing were just not being heard.
So taking all of that into consideration, I’ve been working on developing and continuing to develop the five P process to satisfy both sides of the conversation.
If you’re not familiar, the five P framework is purpose, people, process, platform, and performance.
It’s meant to give you, at a high level, a sense of direction to help you make decisions.
So at the very least, start with your purpose statement: Why am I doing this thing? What is the goal? What is the problem I’m trying to solve? What is the question I’m trying to answer? So that when you start your development project, whether it be prompt engineering, or writing code, or whatever it is, you have a sense of direction, you’re not just sitting down at your keyboard, winging it.
You can totally do that, you can totally wing it, but what I’d like to challenge people with is how much time, budget, and resources are you comfortable wasting by winging it? By actually putting a very simple, high-level plan together, there’s less waste that’s going to happen.
So you start with your requirements gathering.
You can do a simple user story; a user story borrowed from agile development is a three-part sentence: “As [their persona], I want to [action], so that [outcome].” So you could say, at a very high level, “As a marketer, I want to write a prompt so that I can analyze my customer data.” Okay, it’s pretty vague, but now you have a sense of direction because your goal, your “so that,” your outcome, is analyzing your customer data.
And so if you are putting things into your prompt that don’t help you analyze customer data, then you’re going in the wrong direction.
Christopher Penn 4:23
Got it.
So let’s talk about a real-life example, one I actually used this morning.
My user statement was: “As a lazy salesperson, I want to write a thorough and comprehensive scope of work using existing templates, using our existing MSA, using the input call they already did because they don’t want to do it again, so that I get a comprehensive scope of work to present to a prospective customer that Katie will not be angry at.”
Katie Robbert 4:57
That’s a good outcome.
Christopher Penn 4:58
That’s a good outcome.
One of the things that we talked about is, in that requirements gathering, you have specified, like, “Yeah, for example, on every scope of work, it should be required that electronic payment is mandatory, like, we’re not taking paper checks.” That’s one of the things that goes into that requirements gathering, to say, “Okay, if we’re going to have a scope of work, it has to have this.” So if I’m going to build a prompt or series of prompts for generative AI to accomplish this task, that has to be in there.
So my purpose is: save me time, right? Because writing a scope of work typically takes me four to six hours.
To me, it’s an exercise not only in trying to remember what I talked about in the first place, but then the long checklist of all these things that I have to remember to do.
And even though we have templates, it’s still a lengthy process.
The people involved are obviously the customer, there is—but there’s also you as a stakeholder.
It was like, “Okay, I don’t want to catch up projects,” there’s me, the person writing the thing.
The process is the templates themselves.
So we have—thanks to our crack legal team—a really good master services agreement, we have a really good template for a scope of work, we know what we want the outcome to be.
And so the platform in this case would be Gemini or the large language model of your choice.
And the performance, from a requirements perspective, is a comprehensive scope of work that matches what the customer expects but also fulfills the requirements that we’ve set down.
So just from developing a prompt, going through those five Ps makes it pretty clear, these are all the things that have to be involved.
Katie Robbert 6:43
Right.
To your point, writing a scope of work, for a business like ours, shouldn’t be that time-consuming, because, sure, every client wants something a little bit different, a little bit unique to them, which totally makes sense.
However, behind the scenes, our approach is going to be pretty similar.
You know, we’re always going to, you know, step one, take a look at the systems, step two, audit the data, step three—we’re always going to have the same sets of steps.
You should be able to find those patterns very easily so that you can build a prompt that is going to help you.
You already have 70% of the information across 20 different scopes of work.
So it’s a matter of taking the information from those 20 different contracts and finding the commonalities to find one base set of steps that we always do.
And then it’s that 30% that’s unique to each individual client.
Christopher Penn 7:45
Exactly.
So from a prompt engineering perspective, from a code writing perspective, we would then transform this into a prompt.
And the framework that we use is what we call the RACE framework: Role, Action, Context, Execute, where we tell a model, “Hey, here’s kind of who you are going to be,” whether it is a sales expert or you name it, there’s some form of definition of what we think the model should be.
I’m gonna go ahead and bring up our framework here so you can see it.
You can get a copy of this at TrustInsights.ai/promptsheet, if you would like it, it’s a PDF.
So the Role is who’s the model going to be.
The Action is what they’re going to do.
The Context is background information.
And the Execution is the styling.
Again, like I said, this is available as a PDF.
So if I’ve got the requirements done, I’ve filled out the five Ps, I can start building this code, and I would build it exactly like this.
Here is who the model—who you want the model to be.
And that role matters because so much of today’s models have been trained on existing information like articles, and books, and webpages and blogs, and you name it, that telling a model who it wants to be narrows down what kind of information the model should be considering from its vast memory to accomplish the task.
In software terms, this is almost kind of like loading libraries.
If you’ve ever done any kind of coding in R or Python or PHP, you might specify at the beginning of your code, you’ll import this library or load this library.
You’re kind of doing the same thing here.
You’re loading libraries, you’re loading roles, you’re loading conceptual ideas of who this model should be.
Next, Action.
What do we want the model to do? We want the model to help us, in this case, write a scope of work.
However, one of the things that we’re going to want to specify is this scope of work shouldn’t just be a random act.
We have the existing documents, so we want to specify, “You’re going to write a scope of work, but you’re going to do it with our templates.” This is a really important point, particularly if you want to use generative AI for more than just, you know, write a blog post.
If you’ve already got templates, you should be feeding them to your AI systems, you should be saying, “Here is the template that we’re going to use” so that it doesn’t have to guess, because you get errors, hallucinations, and things when you make the model guess.
When you don’t make the model guess, it does so much better.
So what does this look like? I will show you very briefly what this kind of prompt looks like, and it looks like this: “You’re gonna roleplay Sam, the sales expert, a solution selling certified coach and sales consultant,” right? That is our role, so that’s loading those libraries.
“Today, starting the action, we’re going to build a scope of work for my company, Trust Insights, that will be thorough, compelling, and helpful.
Before we begin,” now we move into context, “I’m going to give you a copy of our master services agreement so that you know what doesn’t need to go into scope for work because it’s covered elsewhere.
I’ll also provide you with our scope template.” And then the execute statement for this first part, “Read this through the scope of work template, then we can begin discussing completing the various sections of it.
Do you have any questions before we get underway?” So that’s the RACE framework in action for this specific task.
Katie Robbert 11:18
I always come back to this, but I think it’s a point that bears repeating: you could give this exact same set of instructions to a human and find yourself being very successful.
You made the point of, “If you have templates, use them,” because if you’re making the machine guess, you’re not going to be happy with the outcome.
The same is true when you’re working with people.
If you have a set of templates that you want the person to follow, give that to them.
I can’t tell you how many times I’ve been asked to do something, and I complete the task, and I find out later, “Well, we have a template for that, or we have a structure for that.” “Well, you wasted my time, why couldn’t I just use that in the first place instead of getting frustrated with you not liking my outcome?” I keep bringing it up because I find it an interesting parallel between managing people and managing machines.
You can learn a lot from both of those instances because you’re saying, “In this context, I need you to put on this particular hat, I need you to be this person, I need you to take this action, here’s the contextual information, here’s all the background that you’re going to need so that I can set you up for success and you can be the most efficient.
Now please go do the thing that I have asked.”
Christopher Penn 12:37
Exactly.
And the thing is that this is not rocket surgery, right? This is not something that, to your point, people should be used to this kind of work.
They should be used to these templates.
So if you’re asking someone to get good at prompt engineering, you’re really just asking them to get good at giving good instructions.
Katie Robbert 12:59
If you are someone who struggles to articulate what you want, or has made comments like, “Well, I don’t know how to delegate,” prompt engineering is going to be tricky for you.
And so that’s why we offer up this RACE framework to help you get started, because it can help you figure out, “Okay, what is the information that I need to articulate to this machine to set it up for success?” Because if you say, “Hey, generative AI, I want to write a blog post about B2B marketing.
Can you write a 500-word blog post?” it’s going to say, “Yeah, absolutely, I can write a blog post about B2B marketing.” It’s not going to say no, it’s going to do exactly what you’re asking.
You may not like the result because you have not been specific, you have not said who the audience is, you have not set the points that you want to cover in this thing.
It’s the same thing as if Chris says to me, “Hey, Katie, can you write a blog post about email marketing?” Like, “Yeah, sure,” and I’m going to put my spin on it because he didn’t specify what he did or did not want me to do.
So when I hand it back to him, he says, “You know, I’ve heavily edited this thing, this is not at all what I thought you were going to do.” “Okay, great, you didn’t tell me what you thought I was going to do, so that’s on you, not me.
But it’s also on me for not saying, ‘Well, what do you want me to do?'”
Christopher Penn 14:22
Yep.
So I put this into Gemini, Google’s Gemini 1.5 Pro, I added our scope of work template and our MSA, and Sam, the sales expert says, “Hey, I’ve reviewed these things, it’s fantastic how these standardized documents in places streamline the outputs.” And so now Sam is like, “Okay, I’m ready to start taking instructions.” And so what I would say next, maybe just for clarity is, “Just to be sure, can you outline the major sections of our SOW template that require my input so that we are on the same page?” Again, the language here is eerily similar to exactly how you’d talk to a new sales intern you just brought on, like, “Hey, did you read the—I gave you some stuff before the meeting, did you read it?” And if the intern is like, “Yeah, and you want to know about strategy, tactics, prerequisites, stuff like that? Cool, you did read it,” or introduce it like, “Maybe read this for the meeting.” So Sam, the sales expert has said, “Okay, it’s pretty clear what you want me to do: strategy, tactics, prerequisites, deliverables, timeline, and fees.” What we would do now, if you were going to start using this prompt structure, is start telling Sam, “Okay, well, here’s—here’s Jim, we want to do more of this.
Here’s—the tactics are usually the client even is,” and so on, and so forth.
One of the things that I found to be very successful with this style of prompt is, if I’ve already got the information, like from a transcript of the input call, I’m going to say, “Here’s the input call, take all the information from the input call and slot it into these sections.” And I can’t show that because the one I have is under NDA, but it does that very, very well, and then I just clarify and work with it.
So from a prompt engineering 101 perspective, if you start with the RACE framework, and you start with templates you’ve already got, you’re going to get success very quickly.
Katie Robbert 16:19
It’s true.
And as I’m thinking, as I’m watching you do this, ideally, you just say, “This is my client, this is the problem they’re trying to solve.” And if you’re doing this correctly, if you’ve given enough background, contextual information about who you are as the consultant, what your company does, if you build all of that into your master prompt, you know, you could say, “For context, here’s our list of services, here’s how we approach them, here’s our general price points,” like, “Here’s all of the information,” you can build that into your prompt library to reuse over and over again.
So that basically, you’re just copying and pasting the prompt, and then saying, “Here’s the customer, here’s the problem they’re trying to solve,” hit the go button, and it fills in the template for you because it has all of that background information.
Christopher Penn 17:14
Exactly.
If you want to—this is probably not 101—but if you want to level up what you’re doing with these tools, particularly with this interface or with OpenAI, are custom instructions.
You can say—you can ask, “Okay, well, what are some best practices for writing, in this case, good scopes of work?” And then you’ll see a little section here that says “system instructions,” and you will paste those best practices in there, and you’ll say, “Okay, for anytime we’re going to engage in this particular dialogue, these are the rules, these are the things”—in our case, I actually have a whole prompt for reviewing scopes of work because I forget things—and I’ll just put that up on screen here so you can see it.
“Requirements: billing contacts and acceptance of electronic payments must be on the SOW.” This was Katie’s thing from—we’ve talked about requirements gathering in the last episode and at the beginning of this one.
There it is.
“One thing that we’ve had problems with in the past”—and by “we,” I mean me—”ensure the strategy, tactics, and deliverables are aligned.
If something’s mentioned in tactics, there should be a corresponding deliverable.
If something’s mentioned in deliverables, there should be a corresponding tactic.
Highlight orphaned tactics or deliverables, things that don’t have a corresponding partner,” right? That’s something that I screw up a lot.
So part of prompt engineering is not just trying to write the perfect prompt the first time, but it is incorporating your knowledge and your experiences and the feedback that you’ve gotten into your prompts so that they get better over time.
“Ensure billing is correct, right, the fees should match the invoicing.” That’s clearly something I have screwed up over time.
“Check the prerequisites, do these seem logical and complete when examining strategy and tactics?” That is another thing that I have screwed up over time.
So again, this is part of prompt engineering 101: all the things you’ve done wrong or gotten corrective feedback about go into your prompts.
Katie Robbert 19:11
I feel like—I hope that you have like a chapter in your prompt library of things that, you know, help Katie sleep better at night, and this is definitely one of them.
Christopher Penn 19:21
But again, this is—anytime—this is one of the secrets of prompt engineering.
Anytime you get constructive feedback on any of your work, if you’re using generative AI, it should go back into your prompt.
For example, the other day we were having an internal meeting talking about, “Why do we not focus on SEO enough? Like, why don’t we think about that?” And there were a variety of reasons.
But when I went back to revise the transcript, the outlines for this series, I added a new paragraph.
I said, “What are the best practices for podcast SEO?” and it listed them, to transform this into a pro prompt, I pasted that right back into the documents, “Okay, revise the outline for this four-part series now using these SEO best practices.” So even though I might forget, as a human, “Oh, yeah, SEO is important, we should consider it,” now if I put it into the prompts, the machine remembers that even if I don’t.
And again, that’s one of those key things about prompt engineering is you’re writing code.
If there’s stuff that you need to do, that’s part of that process, bake it into your code, bake it into your prompts.
Katie Robbert 20:33
What’s interesting here is, in doing this, you’re actually strengthening your human-to-human relationships because you’re starting to build that trust.
What I mean by that is, you know, so Chris, we’ve—we all forget things.
And so I know you’re, you know, sort of picking on yourself, but we all forget things.
In this example, if you happen to be the one who forgets to align all of the things in the contract, I know, historically, if you’re writing a contract, I have to carve out time to then go through it and refine it.
And that’s more time out of my day that I don’t have to do other things.
However, if I start to see, “Okay, you’re taking the feedback, you’re building this into the prompt,” you know, when I start to see these revised scopes of work, because of the way that you’re using generative AI, there’s less and less of those things, I’m seeing that you’re taking the feedback, and that’s more trust that I’m building with you writing the scopes, and less time that I have to spend on double-checking to make sure things are missed.
That’s management 101: Is the person taking the feedback? Are they not only taking the feedback, but implementing the feedback and making the corrections so that you, as the manager, get to be less hands-on, less micromanaging, and just allow the person to think, because you are—you’ve been assured that they can do the thing.
It’s—it not only strengthens the content and the contracts, and however you’re using it, but it also strengthens that interpersonal relationship that you have with your other team members because it’s less things for them to worry about.
And that’s one of the benefits, those side effects, of using a system this way.
Christopher Penn 22:33
Exactly.
Because it builds—even though generative AI is the platform—we’re really talking about upgrading the process that interacts with the platform based on what the people and the purpose want.
Katie Robbert 22:46
It’s true, it starts to build that confidence over time of, “I’m seeing things done correctly, I’m seeing things done the way that I’ve asked for them to be done, I’m seeing things done in a way that benefits the company.” So I’m building that confidence both with people who are executing it and the systems that are doing the work.
Christopher Penn 23:06
Exactly.
So to wrap up on prompt engineering 101, here’s the key thing for where you’re going to get benefit out of generative AI: Where do you use templates and checklists today? Where do you have any already defined process or an already defined output? Generative AI is capable of mimicking output very, very closely.
You can dramatically cut the amount of time that you spend while maintaining or increasing the quality of outputs.
It may not be “write this blog post,” right? That’s kind of like taking a Harrier jet to the grocery store.
Yes, it can do that, but there’s a gross under-use of the capabilities of the tool.
Look for the processes that consume the most time, or—here’s an easy way to tell—look for the ones that you don’t like doing, and say, “Okay, how can I get this to be done by generative AI?” It will improve your output, and you will find that your prompt engineering will be dramatically better because of it, because you’re like, “I don’t have to do this anymore!” If you’ve got some prompts that you want to talk about, some ideas about how you might use the RACE framework, hop on over to our free Slack group, go to TrustInsights.ai/analyticsformarketers, where you have over 3,000 other marketers asking and answering those questions every single day.
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Thanks for tuning in, I’ll talk to you on the next one.
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Trust Insights (trustinsights.ai) is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.
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