All right.
You made it this far.
You already know how to build a solid prompt from scratch.
So far, we've covered the structure, the role assignment, how to give good context, and how to write
with intention.
Now here's where things get even more interesting, because now it's time to flip everything we've learned
on its head.
In this lesson, we're going to take the insights we've gained so far and remix it.
We're going to look at how to let AI co-create your prompts and introduce smarter overall techniques,
which will save you hours, boost the effectiveness of your outputs, and open up some awesome new possibilities.
Okay, so we just learned how to build a prompt from scratch.
And yeah, it took a little time, right?
It was structured, it was intentional and it worked.
But now we're about to remix the whole approach.
But what happens if we ask ChatGPT to build the prompt for us?
So now let's use the six step checklist we used in the last lesson.
Use ChatGPT upload function and ask the following.
I want you to create the perfect prompt for crafting brand guidelines for my brand as a marketing freelancer.
Please use the attached checklist to do so, and please use the Golden Circle framework from Simon Sinek,
while also indicating which section of the perfect prompt matches the elements from the checklist.
The Golden Circle Framework by Simon Sinek helps you build a brand by starting with your why, your
purpose and then your how, your unique approach and finally what the actual product or service you
offer and boom!
Now let's look at our output.
This is what is called meta prompting, and it's exactly what it sounds like basically a prompt for
prompts.
This is really cool because the AI turns into a brainstorming partner, your editor, and your coach
all at once.
This technique is just one example of how we can completely change the way we create content.
And that's the power of AI.
The normal way of doing things by hand or manually is now totally up for reinvention.
So now, with that little mind melter out of the way, Let's go over some key distinctions I want you
to have around prompting.
Let's start by introducing something that's key to excelling with AI the mindset of iteration and troubleshooting,
having the habit of reviewing your output, reflecting on what worked and didn't, and fine tuning your
prompt is what will ultimately guarantee your AI proficiency set in another way.
It doesn't matter if you know all the different prompting frameworks.
If you don't practice refining your prompts, the more you experiment and iterate, the better your
results will get.
So remember, it's all about experimenting, reflecting, and adjusting to get closer to exactly what
you're looking for.
Now that we've set the mindset, let's dive into the foundational prompting techniques you'll use all
the time.
First, modular or reusable prompts.
These are templates you can return to again and again.
We'll dive into this in the upcoming lessons.
Instead of writing a new prompt from scratch.
Every time you've got a formula to plug and play, and with time, you'll get to know what your go to
prompts are.
And from there, it's easy to notice the patterns of what works and what doesn't.
And here's an example of a modular prompt.
Act as this specific role.
Analyze this specific input and respond in this specific format using this specific context.
Next up is chat memory awareness.
ChatGPT remembers things within a thread, but not across threads.
Unless you're using the memory feature, which we will show you how to enable in an upcoming lesson.
This means the prompts will likely behave differently depending on whether you're starting fresh or
building on an ongoing conversation, and if you have the Memory awareness feature enabled.
So if your output's getting weird, or you just want ChatGPT to ignore the context of a previous conversation,
this is probably the right time to just start a fresh new thread or even a temporary chat.
Now let's talk about cleaning data.
Say you paste in a huge credit card statement and want to figure out how much you spent on travel.
First, provide your input text data and ask the AI model to clean it and remove any unwanted information
that might interfere with your desired output after that step.
Then you're ready to continue with the proper input, aka clean data.
Now, to understand this in action, let's hop over to ChatGPT.
So we're going to start by adding a credit card statement, which just has all of the raw data from
how the credit card was used.
Then we'll add a prompt instructing ChatGPT on how to clean it.
Here's the prompt.
Remove any line items not related to travel, keeping only flights, hotels, rental cars and public
transport.
ChatGPT is going to go through and remove those specifically unwanted items.
Create the clean output and after we'll have the correct input to work with to achieve our goal, which
is only adding up specific expenses from our example credit card statement, which would otherwise be
extremely time consuming to do so.
Now that we have our ideal text information, we can use this prompt, add up all the expenses and summarize
them by category.
As you can see, we got the output we wanted.
So this is an example of how the input that we gave ChatGPT, which is all of the data on the credit
card.
If we didn't clean the data, our prompt wouldn't work.
It wouldn't give us this result.
So I hope that you can see by taking the time to clean your data, to make sure that it's only taking
into account what we want to know.
It's able to give us a useful output.
And this isn't just for finances.
You can use this with any long video transcripts, messy meeting notes, or any kind of extremely detailed
information or complex documents which need to be refined and cleaned first so you can get the output
you want.
And lastly, we have prompt priming.
This one's crucial.
You want to give your prompt everything it needs to succeed.
Simply put, priming means adding relevant context, whether it's a block of text, PDF, screenshot,
or even a URL.
The more useful detail you feed the AI, the better and more specific the results are going to be.
I use the term useful because we could feed ChatGPT context all day, but if it isn't specific data
leading towards a desired output, then we're just adding extra noise, almost like the opposite of
cleaning data, which we just talked about.
We're already doing this in the course, but I want to spend a little time with an example specifying
when and how to effectively use this correctly.
Because of how useful this is in your day to day prompts.
One way of priming is just putting your written context into the initial input.
Here, we dropped Wimbledon's 2024 tennis match data from Wikipedia by copying and pasting it without
any formatting, and told ChatGPT we are dropping data without any instructions to act on, along with
the following prompt.
Before we proceed with instructions, I will provide Wimbledon's 2024 match data.
Then we asked ChatGPT to only reference the data that we input, and the prompt we used was using only
the data provided in this thread from Wimbledon 2024 match data.
Predict who will win Wimbledon 2025.
Don't research the web for extra data.
Now looking at this, what we get is an analysis based on 2024 performance alone for both men's singles
and women's singles, and some dark horses, aka some underdogs that should have a potential shot at
first place.
So with this first example out of the way, another way of priming is uploading reference material.
In this example, I uploaded two screenshots from Amazon's best seller product page and instructed that
we are first priming the thread before providing instructions for ChatGPT to proceed.
After that, we gave product descriptions of three best selling items, again instructing ChatGPT that
before we proceed with anything, we will be adding these inputs as additional context.
Even after instructing ChatGPT that first we are adding information before providing instructions,
it automatically analyzes the content of the three product descriptions and gives us insights, a table
highlighting common traits and potential next steps.
But to make full use of the priming process that we just went through, we still have to prompt ChatGPT
with our instructions.
So as a final step, you can instruct ChatGPT to analyze the input provided and then propose what all
the best selling items have in common that we can incorporate into our own products.
Of course, this then gives us a thoughtful analysis of multiple aspects that we can consider and implement,
along with a cheat sheet for our specific Amazon listing strategy.
What these prompt priming examples show us is that if we just ask ChatGPT something like who's going
to win Wimbledon?
Or how do I make a best selling Amazon product without giving it the right details to pull from?
We're going to get a generic response, which makes total sense, right?
I mean, we're basically giving ChatGPT our own set of mini training data.
So here's where we're at.
You now know how to prime and prompt to create better outputs, use the proper techniques, clean your
input data and provide the right context for smarter results.
Next, let's continue diving in to how to work with and use different prompting frameworks.