Learn how to get GPT and other LLM chat to give you perfect responses by mastering the techniques of fast engineering. Anu Kubo is one of our most popular instructors, and in this course, she will teach you the latest techniques to maximize your productivity with large language models. Hello everyone and welcome to this course on fast engineering. My name is Anu Kubo, and I am a software developer and course explorer at FreeCoCamp and also on my own channel.
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This will be a unique course for me because there will be less coding involved but more understanding of fast engineering topics and why some companies pay up to $335,000 per year according to Bloomberg for people in this profession. And no, coding background is not required. So what are we waiting for? Let’s do it. In this course, we will learn what fast engineering actually is, get a brief introduction to AI, look at large language models or LLMs like GPT chat, look at text-to-image models in the middle of the journey, look at evolving models. This includes text-to-speech, text-to-audio, or speech-to-text as well as fast engineering mindset, best practices, zero-shot commands, multi-shot commands, thought chaining, AI hallucination, frustration, text embedding, and also end with a quick intro to GPT chat. So let’s start by looking at what fast engineering actually is. In short, fast engineering is a career that has emerged behind the rise of artificial intelligence. It involves human writing, refining, and optimizing commands in a structured way.
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This is done with the goal of maximizing the interaction between humans and AI. Not only that, a fast engineer is also required to constantly monitor these commands to ensure their effectiveness over time as AI progresses. Maintaining an up-to-date fast library will be a requirement imposed on fast engineers as well as reporting findings and, in general, being thought leaders in this field. But why do we need it? And how does it relate to AI? Before we continue, let’s make sure we have the same understanding of what AI actually is. Artificial intelligence is the simulation of human intelligence processes by machines. In my opinion, simulating as artificial intelligence is not something that can be felt, at least not yet, which means the simulation cannot think for itself as much as it appears. Often, and this certainly happens with tools like GPT chat, for example, when we say AI, we are only referring to the term called machine learning. Machine learning works by using a large amount of training data which is then analyzed for correlations and patterns. These patterns are then used to predict outcomes based on the given training data. As an example, here we provide data that says if a paragraph looks like this with a title like this, then the paragraph should be categorized as international finance. The second paragraph should be categorized as earnings report, and so on. With some code, we should be able to train our AI model to correctly guess the content of the next paragraph. And that’s it. Of course, this is a very basic example and we would need more data than just five paragraphs here, but you get the idea. If you want to create your own AI model and have a better understanding of machine learning concepts as a beginner, please check out my video on my channel, Code with Anya Kubo. Currently, rapidly evolving AI techniques can create realistic text responses and even images, music, and other media thanks to the abundance of training data and talented developers working on it today. Why is fast engineering beneficial? With the rapid and exponential growth of AI, even its architects struggle to control AI and its outputs. It might be somewhat difficult to understand, but think of it this way. If you ask an AI chatbot what four plus four is, you would expect the answer to be eight, right? The result of eight is undeniable. However, imagine you are a young student trying to learn English. I will show you how different responses can be based on the cues you give and in turn on your learning experience. For this example, I will be using the GPT-4 Chat GPT model. So let’s start with the basics. If you type, fix my paragraph and then paste a poorly written paragraph like this, today is an amazing day for me. I went to Disneyland with my mom. But it would be better if it didn’t rain. Good, a young English learner has a better sentence, but the sentence stops there and the learner is left on their own. And honestly, the sentence is not that great. What if the learner could get the best sentence from a teacher who understands their interests to keep them engaged? With the right cues, we can actually achieve that with AI. So let’s give it a try and write a command to do this. So here’s the command I will give. I want you to play the role of an oral English teacher. I will speak to you in English and you will reply to me in English to practice my oral English. I want you to keep my replies neat, limiting them to 100 words. I also want you to correct my grammar mistakes and typos firmly. And I want you to ask me questions in your replies. Now let’s start practicing. You can ask me a question first. Remember, I want you to correct my grammar mistakes and typos firmly. So that’s my command. You can also go further and ask it to correct your factual errors as well, which I think would be a nice addition to the helpful cues for young learners. Okay, so here’s the prompt. And now let’s let it do its job. And the good thing is, now it’s much more interactive. As you can see, it asks questions and tells you what to do and will correct you if needed. So you are interacting with AI. It gives you suggestions and you keep learning. It’s a truly different experience thanks to the command we wrote. Pretty cool, isn’t it? We will delve into many of these concepts, but first let’s start with the basics. Linguistics. Linguistics is the study of language. It focuses on everything from phonetics, which is the study of how speech sounds are produced and perceived, phonology, which is the study of patterns and changes in sounds, morphology, the study of word structure, syntax, the study of sentence structure, semantics, the study of linguistic meaning, pragmatics, 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 relationship between language and society, computational linguistics, or the study of how computers can process human language, and psycholinguistics, or the study of how humans acquire and use language. Quite a lot. Linguistics is key to fast engineering. Why? Understanding the nuances of language and how it is used in different contexts is crucial for crafting effective prompts. Not only that, knowing how to use universal grammar or language structures will make the AI system provide the most accurate results. As you can imagine, the vast amount of training data is likely to have been trained using standard grammar and universally used language structures. So sticking to standardization is the key. Language models. Imagine a world where computers have the power to understand and generate human language. A world where machines can chat, write stories, and even compose poetry. In this magical world, language models come into play. They are like digital wizards that can understand and create human-like text. Language models are smart computer programs that learn from a vast amount of written text data. It takes books, articles, websites, and all sorts of written sources, allowing it to gather knowledge about how humans use language. Just like a language expert, it becomes an expert in the art of conversation, grammar, and style. But how does it work? Well, imagine you give it a sentence. The language model will then analyze the sentence, checking the word order, its meaning, and its appropriateness. The language model will then generate predictions or continuations of the sentence that make sense based on its understanding of the language. It will string words together one by one, creating a response that seems as if it were made by a human. It’s like having a language expert by your side, always ready to help and engage in conversation. Now, you might be wondering where these language models are used. They can be found in various places, ranging from your smartphone’s virtual assistant to customer service chatbots and even in the world of creative writing. They help us find information, offer suggestions, and create content. However, it’s important to remember that although language models have incredible capabilities, they still rely on humans to create and train them. In fact, they are a blend of human intelligence and algorithmic power, combining the best of both worlds. Let’s take a look at the history of language models, starting with the first AI, Eliza, in the 1960s. Eliza was an early natural language processing computer program created from 1964 to 1966 at MIT by Joseph Weizenbaum. Eliza was designed to simulate conversations with humans. Eliza had a special talent for mimicking the 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 pre-determined patterns, each associated with a specific response. These patterns were like magical incantations that allowed Eliza to understand and respond to human language. When you engaged in a conversation with Eliza, Eliza would carefully analyze your input, searching for patterns and keywords. It would then transform your words into a series of symbols, searching for patterns that matched those symbols in its repertoire. Once a pattern was detected, Eliza would work its magic by transforming your words into questions or statements aimed at exploring your thoughts and emotions. As if Eliza were holding up a metaphorical mirror, encouraging you to delve deeper into your own thoughts. For example, if you were to say something like, “I feel sad,” Eliza would detect the pattern and respond with a question like, “Why do you think you feel sad?” This encouraged reflection and introspection, much like a caring therapist would do. But here’s the fun part. Eliza didn’t actually understand what you were saying. It was just a clever illusion. 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 predetermined rules. However, even though Eliza was a simple program, people often became captivated by its conversational abilities. They felt listened to and understood, even though they knew they were talking to a machine. It felt like having a digital confidant who was always ready to listen and provide gentle guidance. Eliza’s influence was significant, 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 a humble beginning to a grand adventure in the world of conversational AI. Fast forward to the 1970s, when a program called Shudlu emerged. It could understand simple commands and interact with a virtual block world. While Shudlu wasn’t a language model per se, it laid the groundwork for the idea of machines understanding human language. But the actual language models started 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 2018, the fast GPT version was created by OpenAI. It was trained on a massive amount of text data, absorbing knowledge from books, articles, and most of the internet. GPT-1 was a glimpse of things to come, an impressive language model, but small compared to its descendants that we use today. Over time, the story continued with the arrival of GPT-2 in 2019, followed by GPT-3 in 2020. It is a giant among language models, equipped with over 175 billion parameters. GPT-3 wowed the world with its unparalleled ability to understand, respond, and even generate creative writing. The arrival of GPT-3 marked a true turning point in terms of language models and AI. At the time of writing this, we now also have GPT-4, trained on almost the entire internet, not just a large outdated dataset, as well as Google’s BERT and many more. It seems we are only at the beginning stages when it comes to language models and AI. So learning how to harness this data with fast engineering is a smart move for anyone right now. Fast engineering mindset. When it comes to crafting good prompts, the best practice is always to use the right mindset. Essentially, you just want to write one prompt, right? And there’s no need to waste time and effort writing multiple different prompts until you get the desired result. So basically, it’s like when you search for something on Google, right? How good is your Googling ability now compared to five years ago? I assume much better. We have intuitively developed a sense of what to type into Google for the first time to not waste time. Having the same mindset for fast engineering can also be applied. Mikhail Eric from the Infinite Machine Learning Podcast put it well when he said, “I personally like the analogy of a push to design an effective Google search. Clearly, there are better and worse ways to write a question to the Google search engine that accomplishes your task. These differences arise because of the ambiguity of what Google does. We will keep this in mind for the rest of the course.” A brief introduction to using GPT chat by OpenAI. So as I mentioned in this course, as an example, I will be using GPT chat as the model. To follow along or just understand how we will be using this platform, please visit openai. com and go ahead and sign up. I have already signed up, so I will just go ahead and log in and it will take me to the platform where I can choose what I want to interact with. For this tutorial, we will be interacting with chat GPT. So go ahead and click here and it will take you to the platform. Then I will go ahead and switch to the GPT model, which is the latest model. Okay, great. Here you will see all my previous chats. I will just minimize this. And if we want to create a new chat, all we have to do is click the new chat button. Okay, so here we have it. For example, I can ask any question. So what is four plus four? And then hit send. And it will basically give me a response. So now I am interacting with chat GPT. In this case, I can actually continue the previous conversation. So what I can do is say, great. Now, can you add five more? What’s the answer? And it will build upon everything I have previously. Okay, I’m building on what I already know. Great. So as you can see, it asks questions and tells you what to do and will give you corrections if needed. So you are interacting with AI. It gives you suggestions and you keep learning. It’s a truly different experience thanks to the command we wrote. Pretty cool, isn’t it? We will delve into many of these concepts, but first let’s start with the basics. Linguistics. Linguistics is the study of language. It focuses on everything from phonetics, which is the study of how speech sounds are produced and perceived, phonology, the study of patterns and changes in sounds, morphology, the study of word structure, syntax, the study of sentence structure, semantics, the study of linguistic meaning, pragmatics, 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 relationship between language and society, computational linguistics, or the study of how computers can process human language, and psycholinguistics, or the study of how humans acquire and use language. Quite a lot. Linguistics is key to fast engineering. Why? Understanding the nuances of language and how it is used in different contexts is crucial for crafting effective prompts. Not only that, knowing how to use universal grammar or language structures will make the AI system provide the most accurate results. As you can imagine, the vast amount of training data is likely to have been trained using standard grammar and universally used language structures. So sticking to standardization is the key. Language models. Imagine a world where computers have the power to understand and generate human language. A world where machines can chat, write stories, and even compose poetry. In this magical world, language models come into play. They are like digital wizards that can understand and create human-like text. Language models are smart computer programs that learn from a vast amount of written text data. It takes books, articles, websites, and all sorts of written sources, allowing it to gather knowledge about how humans use language. Just like a language expert, it becomes an expert in the art of conversation, grammar, and style. But how does it work? Well, imagine you give it a sentence. The language model will then analyze the sentence, checking the word order, its meaning, and its appropriateness. The language model will then generate predictions or continuations of the sentence that make sense based on its understanding of the language. It will string words together one by one, creating a response that seems as if it were made by a human. It’s like having a language expert by your side, always ready to help and engage in conversation. Now, you might be wondering where these language models are used. They can be found in various places, ranging from your smartphone’s virtual assistant to customer service chatbots and even in the world of creative writing. They help us find information, offer suggestions, and create content. However, it’s important to remember that although language models have incredible capabilities, they still rely on humans to create and train them. In fact, they are a blend of human intelligence and algorithmic power, combining the best of both worlds. Let’s take a look at the history of language models, starting with the first AI, Eliza, in the 1960s. Eliza was an early natural language processing computer program created from 1964 to 1966 at MIT by Joseph Weizenbaum. Eliza was designed to simulate conversations with humans. Eliza had a special talent for mimicking the 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 pre-determined patterns, each associated with a specific response. These patterns were like magical incantations that allowed Eliza to understand and respond to human language. When you engaged in a conversation with Eliza, Eliza would carefully analyze your input, searching for patterns and keywords. It would then transform your words into a series of symbols, searching for patterns that matched those symbols in its repertoire. Once a pattern was detected, Eliza would work its magic by transforming your words into questions or statements aimed at exploring your thoughts and emotions. As if Eliza were holding up a metaphorical mirror, encouraging you to delve deeper into your own thoughts. For example, if you were to say something like, “I feel sad,” Eliza would detect the pattern and respond with a question like, “Why do you think you feel sad?” This encouraged reflection and introspection, much like a caring therapist would do. But here’s the fun part. Eliza didn’t actually understand what you were saying. It was just a clever illusion. 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 predetermined rules. However, even though Eliza was a simple program, people often became captivated by its conversational abilities. They felt listened to and understood, even though they knew they were talking to a machine. It felt like having a digital confidant who was always ready to listen and provide gentle guidance. Eliza’s influence was significant, 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 a humble beginning to a grand adventure in the world of conversational AI. Fast forward to the 1970s, when a program called Shudlu emerged. It could understand simple commands and interact with a virtual block world. While Shudlu wasn’t a language model per se, it laid the groundwork for the idea of machines understanding human language. But the actual language models started 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 2018, the fast GPT version was created by OpenAI. It was trained on a massive amount of text data, absorbing knowledge from books, articles, and most of the internet. GPT-1 was a glimpse of things to come, an impressive language model, but small compared to its descendants that we use today. Over time, the story continued with the arrival of GPT-2 in 2019, followed by GPT-3 in 2020. It is a giant among language models, equipped with over 175 billion parameters. GPT-3 wowed the world with its unparalleled ability to understand, respond, and even generate creative writing. The arrival of GPT-3 marked a true turning point in terms of language models and AI. At the time of writing this, we now also have GPT-4, trained on almost the entire internet, not just a large outdated dataset, as well as Google’s BERT and many more. It seems we are only at the beginning stages when it comes to language models and AI. So learning how to harness this data with fast engineering is a smart move for anyone right now. Best practices. The biggest misconception about fast engineering is that it is an easy job with no science behind it. I imagine many people think it’s just about putting together a sentence once, like fixing my paragraph example we saw earlier. When you start looking into it, crafting effective prompts depends on many different factors. Here are some things to consider when writing a good prompt. Consider writing clear instructions with details in your request. Consider adopting a persona and specifying the format using repeated commands, meaning if you have a multi-part question or if the first response is not enough, you can continue with follow-up questions or ask the model to elaborate and avoid leading its answer. Try not to make your prompt too directive, inadvertently telling the model what answer you expect. This might make the response too biased. And finally, limit the scope of long topics. If you’re asking about a broad topic, it’s a good idea to break it down or limit its scope to get a more focused answer. Let’s take a look at some of these now. To write clearer instructions, we can adopt a more detailed writing style in our questions. And to get the best results, don’t assume the AI knows what you’re talking about. Instead of writing something like, “When is Christmas in America?” which implies that you expect the AI to know which Christmas you’re talking about and which country you mean, this might result in you asking multiple follow-up questions until you finally get the desired result, wasting time and possibly causing frustration. Consider taking the time to write a prompt with clear instructions. So instead of asking when Christmas is, you could write, “When is the next presidential election in Poland?” So let’s go ahead and run this. And there you have it. It’s clear that there is already data available. We don’t need to add any examples or anything like that. So as you can see, zero-shot prompts refer to how we ask models like GPT without any explicit training examples for the task at hand. In the context of machine learning and not just GPT, zero-shot usually means that the model performs a task without seeing any examples of that task during its training. Okay, great. Now let’s take a look at some prompts. So once again, with zero-shot prompts, we give our prompt to the language model and get a response. But sometimes that’s not enough and we need more training.
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So let’s use a few shots and fine-tune our language model by showing it some training examples through prompts to avoid retraining. So basically, in the context of GPT-4 models, we don’t need to do much. We’ve already used all the data we have to ask when Christmas is in America. So let’s go ahead and do some zero-shot prompts. When is Christmas in America? And there you have it. Okay, so it’s clear that there is already data. We don’t need to add any examples or anything like that. So as you can see, zero-shot prompts refer to how we ask models like GPT without any explicit training examples for the task at hand. In the context of machine learning and not just GPT, zero-shot usually means that the model performs a task without seeing any examples of that task during its training. Okay, great. Now let’s take a look at some prompts. So once again, with zero-shot prompts, we give our prompt to the language model and get a response. But sometimes that’s not enough and we need more training. So let’s use a few shots and fine-tune our language model by showing it some training examples through prompts to avoid retraining. So let’s think about what GPT doesn’t know. I guess it wouldn’t know my favorite food. So for example, we check, what is Ania’s favorite food? In plural form, okay. And I mean, it could guess, but no, it just tells me that it doesn’t know. So that’s okay. Let’s stop generating. So now let’s put in some example data. I’ll put it into a little data.
Title: Exploring Ania’s Favorite Foods and AI Hallucinations
Introduction:
In this blog post, we will discuss Ania’s favorite foods and delve into the concept of AI hallucinations. We will also touch upon text embeddings and vectors, which are important techniques in the field of natural language processing (NLP) and machine learning. So, let’s dive in!
Ania’s Favorite Foods:
Ania’s favorite foods include burgers, french fries, and pizza. These are the types of food she enjoys the most. Knowing her preferences, let’s explore some restaurants in Dubai that offer these dishes. Here are a few options that have been updated as of September 2021:
1. Burger Joint: This restaurant specializes in delicious burgers made with high-quality ingredients. It’s a must-visit for burger lovers like Ania.
2. Fry Heaven: If you’re craving some crispy and flavorful french fries, Fry Heaven is the place to go. They offer a variety of toppings and dipping sauces to enhance your fry experience.
3. Pizza Paradise: For pizza enthusiasts, Pizza Paradise is a great choice. They serve a wide range of pizzas with different toppings and crust options to satisfy every pizza lover’s cravings.
AI Hallucinations:
Now, let’s explore a fascinating aspect of AI called hallucinations. AI hallucinations refer to unusual outputs generated by AI models when they misinterpret data. One famous example of AI hallucinations is Google’s Deep Dream project. It transformed images into surreal combinations of dog faces and other bizarre patterns. Deep Dream visualized patterns learned by neural networks and exaggerated or filled in gaps in images.
AI hallucinations occur because AI models are trained on vast amounts of data and make creative connections based on what they have seen before. Sometimes, these connections result in hallucinatory outputs. While these outputs can be entertaining, they also shed light on how AI models interpret and understand data, giving us insights into their thinking process.
Text Embeddings and Vectors:
Text embeddings are popular techniques used in NLP and machine learning to represent textual information in a format easily processed by algorithms, especially deep learning models. In the context of rapid prototyping, text embeddings refer to converting text prompts into high-dimensional vectors that capture their semantic information.
By using text embeddings, we can find words similar to a given food in a large text corpus by comparing their embeddings. For example, if we want to find words similar to “burger,” the computer will look for lexically similar words rather than unrelated ones. This allows us to capture the semantic meaning behind the word. Text embeddings represent words as arrays of numbers, which can be used to compare and find similar texts.
Conclusion:
In this blog post, we explored Ania’s favorite foods, including burgers, french fries, and pizza. We also delved into the concept of AI hallucinations, where AI models generate unusual outputs due to misinterpretation of data. Additionally, we discussed text embeddings and vectors, which are essential techniques in NLP and machine learning for representing and comparing textual information.
We hope you enjoyed this blog post and gained insights into the fascinating world of AI and its applications in understanding human preferences and generating creative outputs. Feel free to explore text embeddings using the OpenAI API and compare them to find similar texts. Thank you for reading, and see you again on the FreeCocam channel!
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