If we were to type something into ChatGPT and thought to yourself, that's not what I meant, well,
you're not alone.
And today we're going to learn how to fix that.
This next set of lessons is going to be a deep dive into prompt structuring and crafting effective prompts.
We're going to break down the core components of effective prompting by going into role context instructions,
formatting boundaries, and examples.
And we're going to use these so that you can understand how to prompt in a way that gets consistent
and effective results.
And with that said, in future lessons, we're going to explore a lot more creative and interesting
use cases.
But first, let's lock in the foundation that everything else is going to build on top of.
All right, so let's start at the top.
Every AI response is simply shaped by three different things the data it's trained on, the model's
architecture and parameters and most importantly, your prompt.
Now those components will determine the output generated by the model that you're using.
We're going to get into the model training and neural networks later in the course.
But since the prompt is the one thing out of these three things that you can control.
That's where we're going to be starting in this lesson.
So what even is a prompt?
Essentially it's just the input that you give the AI model you're working with.
So a question, a task or any kind of request.
But the AI doesn't think in the same exact way that we do.
It's simply kind of scanning your words, matching them to patterns that it's seen before, and predicting
the most relevant responses based on your input.
Now, this is really an oversimplification, but we'll get into all these details later.
Now, throughout this course, we're going to look at a lot of different ways to prompt.
And we're going to take a look at different prompting frameworks across business creativity, content
creation and productivity with multiple different tools.
But before we get into all that, what we're going to be doing is building a strong foundation, kind
of like learning how to steer before you start driving.
So let's start by zooming all the way out and think about prompting like this.
We want to start by defining what your goal is.
What do you want the AI tool to create after that?
Then you write your first draft of the prompt.
And the key here isn't to create something that's perfect, it's to start the process of creating your
prompt.
After that, we want to test it.
So we're going to run it through ChatGPT, evaluate the output and ask if it hit or missed what we were
wanting to create.
If it needs improvement, we can refine it, add clarity, shift the tone, and make it more specific.
The idea here is that prompting is iterative.
So the feedback loop of this is your goal the prompt, testing it, evaluating it, and then refining
it.
This is how you're going to hone your skills throughout this course and get much more sharper and more
accurate results every time.
And now that we have a bird's eye view on that, let's quickly walk through ChatGPT.
It's the most common starting point for people to use AI for the first time.
It's free, easy to use, and has a really simple interface.
So that's why in this course we're going to use this in the first section.
Now it's something to note that everything we teach in these lessons is going to apply to all the different
tools like clod, perplexity and Gemini.
But for now we're going to focus on ChatGPT, and you'll be able to apply what we learn to any of the
other AI tools.
Now, looking at ChatGPT in the center, this is your prompt box.
This is where all of the prompting and most of the work is going to happen.
On the left is your chat history, also called threads.
We're going to cover the entire interface in a future lesson, but this is all you need for now.
And with that, let's try our first simple prompt.
We're going to put in what hotels are in Paris.
If we look at the output this answer is fine.
It's simple similar to kind of a Google search result, but overall pretty basic and not specific to
you.
The prompter.
It's giving us options, but really nothing is personalized.
Now, if we upgrade the prompt, we could say something like, you are a travel advisor who specializes
in helping travelers find unique and high value hotel stays.
I'm planning a trip to Paris with my partner, and we're looking for a boutique style hotels in lively,
walkable neighborhoods with great food nearby.
We care both about comfort and experience, not just price.
Please recommend five hotels in Paris that fit these preferences.
Format your answers as a numbered list with 1 to 2 sentences describing each hotel only include hotels
under $400 per night that offer amenities like a rooftop patio, gym or pool and are within walking
distance of restaurants.
Describe each option in a concise and captivating manner.
Here's an example of what I'm looking for a stylish boutique hotel near the Latin Quarter with a rooftop
terrace.
Known for its cozy vibe and incredible city views.
Now that was a really lengthy prompt, but as you can see, this is a much, much better output.
It's more focused, helpful and aligned with what we actually want.
So that is structured prompting in action.
Now moving on.
Let's break down some of the aspects that make the second prompt that we just used so much more effective.
Now the anatomy of a high quality prompt commonly consists of six core elements.
These are the same elements that a lot of experienced prompters use.
And once you start using them and you see them, it'll be easier to understand why they're more effective.
The first element is role, and this is going to set the A's persona.
For example, you are a writing coach or act like a designer.
This frames how the model should basically respond.
Next is context.
This is your background or the situational info.
What does the I need to know to give us a useful answer, which could be who's your audience?
What are your goals and any other relevant details?
After that, we have instructions.
This is the actual ask that you're prompting.
What do you want the AI to specifically do?
Here you can use specific verbs like list, explain, and write.
The fourth part of this is formatting.
This is where you tell the AI how to deliver the response paragraphs, tables, bullet points, whatever
fits your specific situation.
Then we have the boundaries.
Or we could call this the constraints.
These are the word limits, the tone, budget, time frames, anything that really helps shape the output.
Lastly, we have examples.
This gives the model a reference of what to create.
So you can add a draft or a piece of content as an overall reference for the model, and then instruct
it by saying use this draft as a template or write in the style of this example email.
This will overall give the eye something to mimic and give it context for its output.
Now these six core elements are really going to help you with your prompting.
And I found that context is similar to and really this whole structure in general is similar.
When you're explaining something complicated to a friend or a coworker, you're not going to just give
someone an oversimplified version of instructions or give them an extremely complicated set of instructions
without having some kind of framework.
And if you're giving them something that's hard to do in a real world scenario, you would give them
context on what a good example of something is and something that they can follow so that they can do
their job better.
And prompting is the same exact way.
So this is just the first part of the anatomy of a great prompt.
In the next set of lessons, we're going to put all this together into practice.
So we're going to compare strong versus weak prompts, break down a few key prompting strategies, and
walk through your first hands on exercise.
So you've now got the structure.
Next it's time to build something with it.