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Hey Reader,

It's been a while since I last wrote this newsletter to you.

The last few weeks have been hectic, and I only now have some time to sit down and focus on teaching.

4 steps to succeed in AI in 2024

With over 12,000+ AI tools online, and all these new language models that are coming out, it might feel hard to stay updated.

Let alone if we think about the new image and video generative models, voice cloning tools and use-case specific tools like AI integrations in pretty much every software you already use (Slack, Notion, LinkedIn, Instagram, Zapier, etc. just to name a few).

So in this email, here is the 4 step shortcut to get caught up with AI ASAP.

1/ Learn Prompt Engineering

Text generative models' performance (like ChatGPT, Claude, Gemini, LLaMa, etc) are determined by 3 key factors.

1. Their training data & architecture (the model itself)
2. Extensions they have (like web access with browsing, knowledge base, etc.)
3. Your prompt

If we are only talking about using LLMs in the browser interface and not integrated into systems, you can't control 2 out of these 3 variables.

Because the model's architecture and training data is given and there's nothing you can do about it.

Extensions are also a given, controlled by the publisher of the model.

So the only thing you can really control is how you give commands to these AI tools, and how well you can decide what tasks to use them for.

Just like how a colleague can't read your mind on what you want; you have to effectively communicate with other people when you are asking for help; an AI can't read your mind either (not yet).

Prompt Engineering is a skill of effective communication with AI Language Models. It's the art and science of writing commands that can get you the outputs that you want, reliably (more on this later). If you want to learn it and master it, enroll in the Prompt Master AI Course today!

2/ Master Conversation Design

Once you get the hang of Prompt Engineering, you will not only see its strengths, but also its limitations. For example, that you cannot write a prompt that writes you a book, no matter how hard you try.

Simply because of the limitation of the model's outputs. They can't output that much content once.

Another example problem you can't solve with prompt engineering, is if you need to make a 10-20 page proposal of a project where you have to include many different sections, that would require totally different prompts to ensure high quality.

These problems are where we have to use a Conversation Design technique where we break down the problem into smaller parts, and write our prompts one at a time, getting the AI model to not only focus on one task at a time, but also feeding its context window, so the next prompt can build on top of the previous responses.

Conversation Design is how you can get outputs out of AI Language Models otherwise impossible with a single prompt.

If you'd like to learn about the 6 beginner and 8 advanced Conversation Design methods that I teach and use every day, enroll in the Prompt Master AI Course.​

3/ Configure a Custom GPT

Okay, once you know how to get any kind of input from ChatGPT, whether long or short, it's time to write prompts, and configure custom versions of ChatGPT (called Custom GPTs), that are highly specific for one single task.

Here are a couple of use cases that my students and I put together:

• Prompt Master Tutor GPT: This GPT is designed for helping my students master the course in the school. It's basically a 24/7 available version of a private tutor
• Vegan Assistant: This GPT is configured to help transition to a vegan diet including recipes, tips and empathetic support.
• IG Carousel Writer GPT: I configured this GPT to assist with repurposing content to Instagram. Once I upload any text to it, it will use my framework for creating IG carousels, and repurpose the uploaded content into a different format. (You can use this idea to also create a GPT that writes short-form video scripts, marketing emails, landing pages, etc.)

And the list goes on and on.

How well and reliably these GPTs perform is absolutely based on how well you write the Instruction prompt, because in a Custom GPT, you only have 1 shot to write a good prompt.

And once you can configure a Custom GPT that works well, safely and reliably, you can also configure a chatbot to run on your (or your client's) website. It's pretty much the same thing. One well written prompt, and maybe some extra knowledge base connected to it.

If you want to learn how to create chatbots that are user-friendly and work safely and reliably, enroll in the Prompt Master AI Course, where I'll walk you through it, and you can also watch me critique other students' GPTs, and give feedback on how they could improve them.

4/ Build no-code AI Agents

Okay so let's say you are amazing at configuring these custom GPTs and you can make an AI do most of your annoying tasks in your business or job.

Then you face the next challenge: I still have to open ChatGPT and prompt it, or trigger my CustomGPT somehow.

So a custom GPT is little bit like an employee you have to micro manage, and tell them what to do every single time.

Enter the next, and final level: building AI agents.

These agents can:

1. Sense changes in the environment
2. Make decisions on how to change that environment
3. Take actions that have an effect on the environment

Okay, I know that sounds super vague. Let me give you an example:

Handling Customer Support Tickets

1. The change in the environment is a new support ticket or email from a lead or customer.
2. The decision making is done by a combination of LLM brain and pre-coded flows on how the data is processed + it's also connected to your public company wiki.
3. The action is a response email to the client answering their question, a notification on Slack for your support team and an update in the Support ticket system that this ticket received a response.

Again, HOW WELL an AI agent works all comes down to how well you can put your thinking and human-based customer support process into one or multiple prompts that handle this smart decision making.

As a Make Academic Alliance Partner, we use Make to build no-code AI agents for ourselves and our clients. If you want to learn exactly how we do this, enroll in the Prompt Master AI Course, and you'll also get a bonus 3-month free membership on the Make Core plan, only available to our students.

A lot of our students found success in this course, and this is a course I also taught at 2 universities this year over a 12-week curriculum.

Once you join, you'll also get lifetime access to the Promptmaster Community, where you can meet the other students, ask questions, join our live Q&A calls, get support for your projects and ask questions directly from me.

The entire course is designed to walk you through these 4 steps that I outlined in this email, so if this resonated, you should totally check out the Prompt Master AI Course, and join!

But regardless of how you decide, I'll stay in touch with more educational content, just like before.

Best,

Dave Talas
CEO & Founder of Promptmaster

P.S: In the Prompt Master AI Course, you can master the 4 steps you need to get caught up and ahead of most of your peers and competitors. Enroll today!