Learn how to get chat GPT and other LLMs to give you the perfect responses by mastering prompt engineering strategies.

Introduction to Prompt Engineering

Discover what prompt engineering is and its role in perfecting the interaction between humans and AI.

What is Prompt Engineering?

Get a brief introduction to AI and how it relates to prompt engineering.

Understanding Artificial Intelligence

Explore the capabilities of language models in understanding and generating human language.

The Power of Language Models

Trace the evolution of language models from early AI programs to the powerful models of today.

The History of Language Models

Learn the mindset and best practices for effective prompt engineering.

The Prompt Engineering Mindset

Get a quick introduction to using chat GPT and its capabilities.

Using Chat GPT

A poem reflecting on the growth of a person from childhood to adulthood.

In the garden of our youth

Exploring different formatting options in chat GPT, including creating checklists.

Specifying format and creating checklists

Discussing two types of prompting: 0 shot prompting and few shot prompting.

0 shot prompting and few shot prompting

Explaining the concept of 0 shot prompting and its application in querying models like GPT.

0 shot prompting

Introducing few shot prompting and its use in training the model with a small amount of data.

Few shot prompting

Exploring the concept of AI hallucinations and how they occur in AI models.

AI hallucinations

Discussing text embedding and vectors as a technique to represent textual information for processing by algorithms.

Vectors and text embeddings

Recapping the topics covered in the prompt engineering course.

Conclusion

Learn prompt engineering techniques to get better results from ChatGPT and other LLMs.

✏️ Course developed by @AniaKubow 

⭐️ Contents ⭐️
⌨️ (0:00:00) Introduction
⌨️ (0:00:57) What is Prompt Engineering?
⌨️ (0:01:43) Introduction to AI
⌨️ (0:03:18) Why is Machine learning useful?
⌨️ (0:06:02) Linguistics
⌨️ (0:07:30) Language Models
⌨️ (0:14:01) Prompt Engineering Mindset
⌨️ (0:15:04) Using GPT-4
⌨️ (0:20:07) Best practices
⌨️ (0:30:46) Zero shot and few shot prompts
⌨️ (0:34:32) AI hallucinations
⌨️ (0:36:09) Vectors/text embeddings
⌨️ (0:39:54) Recap

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Anya Kubo is 1 of our most popular instructors.

And in this course, she will teach you the latest techniques to maximize your productivity with large language models.

Hi, everyone, and welcome to this course on prompt engineering.

My name is Anya Kubo and I'm a software developer as well as course creator here on Free Code Camp as well as on my own channel.

This is going to be a unique course for me as there is going to be a lot less coding going on, but a lot more understanding about the topic of prompt engineering, and why some companies are paying up to

a year, according to Bloomberg, for people in this profession And no, no coding background is necessarily required.

In this course, we will learn what prompt engineering is exactly, get a brief introduction to AI, a look at large language models or LLMs, such as chat GPT, a look at text to image models such as mid journey, a look at emerging models.

This would include text to speech, text to audio or speech to text, as well as prompt engineering mindset, best practices, 0 shot prompting, few shot prompting, chain of thought, AI hallucinations, vexed text embeddings, and also end with a quick intro to chat GPT.

So let's start off with looking at what exactly prompt engineering is in the first place.

Prompt engineering in a nutshell is a career that came about off the back of the rise of artificial intelligence.

It involves human writing, refining and optimizing prompts in a structured way.

This is done with the intention of perfecting the interaction between humans and AI to the highest degree possible.

Not only that, however, a prompt engineer is then also required to continuously monitor those prompts to ensure their effectiveness with time as AI progresses.

Maintaining an up-to-date prompt library will be a requirement that is placed onto the prompt engineer as well as reporting on findings and in general being a thought leader in this space.

Before moving on, let's actually make sure we are on the same page about what exactly AI is.

Artificial intelligence is the simulation of human intelligence processes by machines.

I say simulation as artificial intelligence is not sentient, at least not yet anyways, meaning it cannot think for itself as much as it may seem it does.

Often, and this is certainly the case with tools such as chat to GPT, for example, when we say AI, we are simply referring to a term called machine learning.

Machine learning works by using large amounts of training data that is then analyzed for correlations and patterns.

These patterns are then used to predict outcomes based on the training data provided.

So for example, here we are feeding data saying that if a paragraph looks like this, with this type of title, then it should be categorized as international finance.

The second paragraph should be put in the category of annual reports, and so on.

With some code, we should be able to train our AI model to correctly guess what future paragraphs are about.

Of course, that is a super basic example and we would need way more data than these 5 paragraphs right here, but you get the idea.

If you want to build your own AI model and understand the concept of machine learning better as a total beginner, please check out my video on it on my channel Code with Anya Kubo.

As of now, rapidly improving generative AI techniques can create realistic text responses and even images, music and other media thanks to the huge amounts of training data and talented developers working on it today.

With the quick and exponential growing rise of AI, even the architects of it themselves struggle to control it and its outputs.

This might be a bit hard to understand, but think of it this way.

If you were to ask an AI chatbot what is 4 plus 4, you would expect it to say 8, right?

However, imagine you are a young student trying to learn the English language.

I'm going to show you just how different responses can be based on the prompts you feed, and in turn your learning experience.

For this example, I'm going to be using chat GPT's GPT-4 model.

If you were to type correct my paragraph and then paste it a badly written paragraph so just like this today was great in the world for me I went to Disneyland with my mom.

It could have been better though if it wasn't raining.

The young English learner has a better sentence, but it kind of stops there and the learner is just left to their own devices.

And honestly, the sentence really isn't that great.

Anyway, What if the learner could get the best sentences possible from a teacher who understands their interests to keep them engaged with the correct prompts, we can actually create that with AI.

I'm gonna write, I want you to act as a spoken English teacher.

I will speak to you in English and you will reply to me in English to practice my spoken English.

I want you to keep my reply neat, limiting the reply to 100 words.

I also want you to strictly correct my grammar mistakes and typos.

And I want you to ask me a question in your reply.

Remember, I want you to strictly correct my grammar mistakes and typos.

You can also go a step further and ask it to correct your factual errors too, which I think would be an excellent addition to the prompt that will benefit the young learner.

Okay, so let's go ahead and add that to the prompt.

So now this is way more interactive, as you will see is asking you a question and telling you what to do, and we'll provide you with corrections if needed.

So in a way you're communicating with the AI, it's giving you suggestions and you're learning along the way.

It's a completely different experience thanks to the prompt that we wrote.

We're going to be diving into a bunch of these concepts soon, but first let's start with the basics.

It focuses on everything from phonetics, so the study of how speech sounds are produced and perceived, Phonology, so the study of sound patterns and changes.

Syntax, so the study of sentence structure.

Semantics, so the study of linguistic meaning.

Pragmatics, so in other words the study of how language is used in context, historical linguistics, or the study of language change, sociolinguistics, or in other words the study of the relation between language and society, computational linguistics, so the study of how computers can process human language, and physio-linguistics, or the study of how humans acquire and use language.

Linguistics are the key to prompt engineering.

Understanding the nuances of language and how it is used in different contexts is crucial for crafting effective prompts.

Not only that, but knowing how to use a grammar or language structure that is universally used will result in the AI system returning back the most accurate results.

As you can imagine, the sheer amount of data that is trained on is most likely to mostly use the standard grammar and language structure that is universally used.

So sticking to the standardization is key.

Imagine a world where computers possess the power to understand and generate human language.

A world where machines can chat, write stories, and even compose poetry.

In this magical realm, language models can come into play.

They are like the wizards of the digital realm, capable of understanding and creating human-like text.

A language model is a clever computer program that learns from a vast collection of written text.

It takes in books, articles, websites, and all sorts of written resources, allowing it to gather knowledge about how humans use language.

Just like a master linguist, it becomes an expert in the art of conversation, grammar, and style.

The language model will then analyze the sentence, examine the order of words, their meanings, and the way they fit together.

Then the language model would generate a prediction or a continuation of the sentence that makes sense based on its understanding of the language.

It will weave words together 1 by 1 creating a response that seems like it was crafted by a human being.

Imagine having a conversation with the language model as if you were exchanging ideas with a digital friend.

You ask a question, and it responds with a well-crafted answer.

You tell a joke, and it counters with a witty remark.

It's like having a language expert by your side, always ready to assist and engage in conversation.

Now, you may wonder where these language models are used.

They can be found in various places from your smartphone's virtual assistants to customer service chatbots, and even in the creative realm of writing.

They help us find information, offer suggestions and create content.

But remember, while language models possess incredible abilities, they still rely on the humans who create and train them.

They are in fact a fusion of human ingenuity and the power of algorithms, blending the best of both worlds.

Let's start off by looking at the history of language models, starting with the first AI, ELIZA, back in the 60s.

ELIZA is an early natural language processing computer program created from 1964 to 1966 at MIT by Joseph Wiesenbaum.

Eliza was designed to simulate a conversation with a human being.

Eliza had a special knack for mimicking a Rogerian psychotherapist, someone who essentially listens attentively and asks probing questions to help people explore their thoughts and feelings.

Eliza's secret weapon was its mastery of pattern matching.

It had a treasure trove of predefined patterns, each associated with specific responses.

These patterns were like magical spells that allowed Eliza to understand and respond to human language.

When you engaged in a conversation with Eliza, it would carefully analyze your input, seeking patterns and keywords.

It would then transform your words into a series of symbols searching for patterns that match those symbols in its repertoire.

Once a pattern was detected, Eliza would work its magic by transforming your words into a question or statement that aimed to explore your thoughts and emotions.

It was as if Eliza was holding up a metaphorical mirror encouraging you to delve deeper into your own thinking.

For example, if you said something like, I feel sad, Eliza would detect the pattern and respond with a question like, why do you think you feel sad?

It encouraged reflection and introspection just like a caring therapist would.

Eliza didn't truly understand what you were saying.

It used pattern matching and some creative programming tricks to create the illusion of understanding while in reality it was just following a set of predefined rules.

Yet, even though Eliza was a simple program, people were often captivated by its conversation abilities.

They felt heard and understood, even if they knew they were talking to a machine.

It was like having a digital confident who was always ready to listen and offer gentle guidance.

Eliza's creator, Weizenbaum, intended the program as a method to explore communication between humans and machines.

He was surprised and shocked that individuals, including his secretary, attributed human-like feelings to the computer program.

But that is a whole other topic of conversation in itself.

Eliza's impact was profound, sparking interest and research in the field of natural language processing.

It paved the way for more advanced systems that could truly understand and generate human language.

It was the humble beginning of a grand adventure in the world of conversational AI.

Fast forward to the 1970s when a program named Shirdlu appeared.

It could understand simple commands and interact with a virtual world of blocks.

Although Shirdlu wasn't a language model per se, it laid the foundation for the idea of machines comprehending human language.

But the true language models began around 2010, when the power of deep learning and neural networks came into play.

Enter the mighty GPT, short for Generative Pre-trained Transformer, ready to conquer the world of language.

In the year 2018, the first iteration of GPT emerged, created by the company OpenAI.

It was trained on a large amount of text data, absorbing knowledge from books, articles, and a large chunk of the internet.

GPT-1 was a taste of things to come, an impressive language model but small compared to its descendants that we use today.

As time went on, the saga continued with the arrival of GPT-2 in 2019, followed by GPT-3 in 2020.

This was a titan among language models, equipped with a large number of parameters, over 175 billion to be precise.

GPT-3 dazzled the world with its unparalleled ability to understand, respond and even generate creative pieces of writing.

The arrival of GPT-3 marked a real turning point in terms of language models and AI.

At the time of writing, we now also have GPT-4, trained on pretty much the whole internet rather than outdated large datasets, as well as BERT from Google and so much more.

It would seem we are only just at the start when it comes to language models and AI, So learning how to harness this data with prompt engineering is a smart move for anyone today.

The prompt engineering mindset When thinking of good prompts, it is always best to get in the correct mindset.

Essentially you want to just write 1 prompt, right?

And not have to waste time and tokens writing lots of different prompts until you get the result you desire.

So essentially kind of the same as when you Google stuff right?

How good are your Googling skills now as opposed to 5 years ago?

We have grown to intuitively know what to type into Google the first time round as to not waste time.

Having the same mindset for prompt engineering can also be applied.

Mihail Erik of the Infinite Machine Learning podcast says it well when he says, I personally like the analogy of prompting to designing effective Google searches.

There are clearly better and worse ways to write queries against the Google search engine that solve your task.

This variance exists because of the opaqueness of what Google is doing under the hood.

We are going to keep this in mind for the remainder of the course.

A quick intro to using chat GPT by OpenAI.

So as I said in this course, for the examples, I will be using chat GPT's GPT for model.

In order to follow along or just to understand how we are going to

be using the platform, please head over to open AI.com And just go ahead and sign up.

So I'm just going to go ahead and log in.

And that will take me to the platform in which I can choose what I want to interact with.

For this tutorial, we are going to be interacting with chat GPT-4.

And then I'm just going to go ahead and switch to the GPT-4 model, which is the latest 1.

Okay, so great, you will see here or the previous chance that I've had, I'm just going to minimize this.

And if we want to create a new chat, all we'd have to do is click the new chat button.

Okay, So here, for example, what I can do is just go ahead and ask any questions.

And that will essentially give me a response.

So I'm now interacting with chat GPT for On this occasion, I can actually build on the previous conversation.

And it will take into account everything that I have previously, okay, I am building on top of the knowledge that it already has.

So again, this is just a very quick introduction to how to use this, we will be doing a deeper dive into this throughout this course.

So once again, to create a new chat, all you'd have to do is create your chat.

And if you want to delete the old 1, just go ahead and click that Delete button, and that will delete it.

And then a new chat is brought up automatically.

Now you might have seen my course on OpenAI, in which we can also use the API.

And to use the API, just go over to the API references.

And all you would have to do is get an API key.

And then you just go ahead and create an API key.

And that will allow you to interact with the open AI API in order for you to build out your own platforms just as the 1 we saw before.

So if you're interested in that, please do head over to my tutorial on this, again, on the free code camp channel.

Otherwise, let's continue using chat GPT.

So once again, I'm just going to log in and head back to the platform here.

Another thing I want to discuss is tokens, as you might find that you have run out of the free tokens that you have in order to interact with chat GPT.

So in order to do that, I'm just going to show you this I'm going to head over to the documentation and talk to you a little bit about tokens.

So GPT for essentially processes all text in chunks called tokens.

And this token is approximately 4 characters or 0.75 word for English text.

If you want to know exactly how many tokens you are using, you can check it out here.

You can check out the tokenizer tool and it will give you a rough example.

So for example, I can say, what is 4 plus 4.

And with that piece of text, the total count of tokens is going to be 6.

That is exactly how many tokens will be used in order to produce a answer for this request.

So here is a URL for that you can play around with it.

If you want to see your usage, you can head over to account and then you can manage your account.

And this should be able to show you your usage.

So it will show you your usage right here per month.

And then you can of course also add billing in order to carry on using chat GPT in case you use up all your tokens and you can't use it anymore.

So once again this is under account billing overview.

The biggest misconception when it comes to prompt engineering is that it's an easy job with no science to it.

I imagine a lot of people think it's just about constructing a one-off sentence such as correct my paragraph that we saw in the previous example.

When you start to look at it, creating effective prompts relies on a bunch of different factors.

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Prompt Engineering Tutorial – Master ChatGPT and LLM Responses