INBOX INSIGHTS, April 17, 2024: The Prompt Engineering Life Cycle, Using Analytics with AI

INBOX INSIGHTS: The Prompt Engineering Life Cycle, Using Analytics with AI (4/17) :: View in browser

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Prompt Engineering Life Cycle

This week, Chris asked if I could speak about how prompt engineering fits into the software development life cycle (SDLC). It’s an interesting question to tackle. This either assumes that prompt engineering is a type of development, or you can use the SDLC for non-technical tasks.

Either way, let’s see what happens.

For context, the software development life cycle (SDLC) was created in the 1960’s to help break down complex business systems. The purpose, at the time, was for systems development that required data and analysis. As programming evolved, the life cycle also evolved and became the framework we know today.

Every company has their own version of the SDLC, but at the core, the phases are roughly the same. Every basic SDLC has Planning, Design, Development, Testing, and Deployment.

At a high level, it should look something like this:

insert SDLC

The tasks that go into prompt engineering are very similar to the SDLC. Let’s map the steps.

First, you need a plan. What are you going to do? What is your purpose? For this step I would recommend using the 5P Framework. The 5Ps are Purpose, People, Process, Platform, and Performance. This framework will allow you to efficiently gather your requirements. You’ll know if you need supplemental data or buy-in from other stakeholders. You’ll choose your technology and have a measurable outcome. It will make the development of your prompt go more quickly.

Next, you need to design and develop your prompt. You’ll outline your instructions for a generative AI system. This is where a framework like RACE is useful. RACE is Role, Action, Context, and Execution. This framework will walk you through the steps to construct your prompt, making sure you have the necessary information.

You can grab a copy of the RACE Framework here

Once you’ve developed your prompt you need to test the results. This is where you would use the next framework, PARE. PARE is Prime, Augment, Refresh, and Evaluate. You would use this framework to refine your outcome. You do this by asking questions and poking holes in your initial prompt. When you’re engineering a prompt, you want to spend most of your time in this phase. The RACE Framework is a good start, but it’s the questions that you ask the system that will get you the best results. Remember, these generative AI systems will only do what you ask and nothing more.

You can grab a copy of the PARE Framework here

If you’re satisfied with your results, you can deploy it. Here, you’d save your work into your prompt library for future use. As you have new information or your goals change, you’ll want to update, or maintain your prompts. At a minimum, review your prompts quarterly, or more frequently if they are heavily used.

When you put it all together, it looks something like this:

insert PELC

Not too different visually. The steps are the same. The take away here is that you can rely on existing frameworks that you’re comfortable using to get good results. Prompt Engineering is similar to development in that you need a plan that you can test and refine. Thankfully, there are many frameworks that can help you get the best outcome.

How are you developing your generative AI prompts? Reply to this email to tell me or come join the conversation in our Free Slack Group, Analytics for Marketers.

– Katie Robbert, CEO

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Binge Watch and Listen

In this week’s In-Ear Insights, Katie and Chris discuss how the software development life cycle (SDLC) applies to prompt engineering in generative AI, why the prompt development life cycle (or prompt engineering life cycle) is a good idea, and a real-life application of it.

Watch/listen to this episode of In-Ear Insights here »

Last time on So What? The Marketing Analytics and Insights Livestream, we examined data portability and marketing software. Catch the episode replay here!

On this week’s So What? The Marketing Analytics and Insights Live show, we’ll be stepping through the prompt engineering life cycle with live examples. Are you following our YouTube channel? If not, click/tap here to follow us!

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Data Diaries: Interesting Data We Found

In this week’s Data Diaries, let’s talk about analytics and AI. We’ve all collectively spent the last 18 months foaming at the mouth about generative AI – which is a good thing, it’s a transformational technology – but in doing so we’ve forgotten about the vital importance of analytics.

One of the things I talk about in my keynotes on generative AI is that your success or failure with AI will be governed by two factors: the quality and quantity of your ideas, and the quality and quantity of your data. If you have more, better data than a competitor and you have fluency with generative AI, you will deliver better results than someone without that data.

Here’s a simple example. Suppose you’re writing a prompt to help you build a customer profile. Maybe a competitor is doing the same, and you’re both trying to generate a content marketing strategy to earn more audience online.

Let’s say your competitor just uses anecdotal evidence. They say something like “Our ideal buyer is a senior executive at a company with more than $50 million in revenue.” That’s fine, and certainly better than no data at all. However, if you use that fairly generic description as the starting point for your strategic review, you’re probably not going to get a strategy that’s especially well-tuned to your audience.

Here’s an example of what Google’s Gemini returns from this very generic prompt:

Gemini generic results

Is that terrible? No. it’s not off the mark. But it’s also not going to distinguish our content marketing strategy from anyone else’s.

But suppose you went back to the well of analytics and extracted your audience demographics from Google Analytics 4? Suppose you had the ages, genders, and critically the affinities and interests of your audience, like this, which you can export from either Explore Hub or the API:

branding_interest pct_total
Technology/Technophiles 6.214780
Media & Entertainment/Movie Lovers 4.701336
Lifestyles & Hobbies/Business Professionals 4.298825
News & Politics/Avid News Readers/Entertainment News Enthusiasts 4.298825
Media & Entertainment/TV Lovers 4.161971
Travel/Travel Buffs 4.137820
Media & Entertainment/Light TV Viewers 3.944614
Lifestyles & Hobbies/Shutterbugs 3.872162
Food & Dining/Cooking Enthusiasts/Aspiring Chefs 3.477701
Banking & Finance/Avid Investors 3.356947
Shoppers/Shopping Enthusiasts 2.737079
Technology/Social Media Enthusiasts 2.712929
Home & Garden/Home Decor Enthusiasts 2.568024
News & Politics/Avid News Readers/Avid Business News Readers 2.366769
Media & Entertainment/Music Lovers 2.141362
News & Politics/Avid News Readers 2.117211
Media & Entertainment/Book Lovers 2.020609
Sports & Fitness/Health & Fitness Buffs 2.020609
Sports & Fitness/Sports Fans 1.650298
Lifestyles & Hobbies/Family-Focused 1.561745

Would you get a better result, a better customer profile, if you had this information included? Absolutely. And it’s accessible right now, today, in your Google Analytics 4 instance if you’d turned those features on in the past.

What does Gemini return from this?

Gemini GA4 results

This is better. It’s still pretty broad, as a content strategy, but it’s significantly more specific than the generic example.

The key takeaway here is to remember the data, analytics, and insights we leveraged in the time before generative AI, and use that WITH our new generative AI tools in every aspect of our marketing. Today, it’s even easier to make use of that data; in the past, we had to interpret what it meant, process it, and integrate it into our planning. Now? We copy and paste the data into our prompts, and let the machines make use of the data, leveraging the value it’s had all along, but we neglected to use as well as we should have.

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