Category: chatgpt

ChatGPT – Prompts We used the word “prompt” while discussing how users interact with ChatGPT and other Open AI models. In this chapter, we will discuss the significance of “prompt engineering” to enhance model’s accuracy. The way prompts are designed and crafted influence the output of the model in the following ways − A well-designed prompt can guide the model to produce relevant and precise output. Whereas a poorly designed prompt may result in irrelevant or confusing output. This is just a basic chapter on how you can use different types of prompts in ChatGPT to get the exact information you are looking for. We would like you to refer our tutorial where you will find extensive detail on this topic. Prompts and Their Significance Generative AI models can create various things like poems, stories, images, and code as per user request. However, to get the output we want, we have to give these models the right instructions, known as prompts. Prompts, which mainly refer to a segment of text in natural language, are like the guide for the generative AI model”s output, affecting its tone, style, and overall quality. In fact, prompts are the only way a user can direct the output generated by these models. Types of Prompts for ChatGPT The categories of prompts used in ChatGPT act as guidelines or instructions provided to GPT to steer and control specific kinds of responses and conversations. Let”s explore the commonly used prompts and understand the ways they can be beneficial. Instructional Prompts Instructional prompts are commands that direct the model with specific instructions on the desired format or information to include in the response. Let’s see an example below − Roleplay Prompts Roleplay prompts are commands that frame the input as if the model is a character or has specific role, guiding its response accordingly. Consider the following example − Question-Answer Prompts As the name suggests, QA prompts are commands that pose questions to the model to elicit informative or creative answers. Take a look at the following example − Contextual Prompts Contextual prompts provide context or background information to guide the model’s understanding and response. Check the following example − Creative Storytelling Prompts Creative storytelling prompts encourage the model to generate imaginative or creative narratives by setting up scenarios or story elements. Observe how the following prompt works in this example − Conditional Prompts Conditional prompts specify conditions or constraints for the response to guide the model”s output in a particular direction. Explore the following example − Comparison Prompts Comparison prompts ask the model to compare or contrast different concepts, ideas, or scenarios. Consider the following example − Instructive Prompts Instructive prompts clearly instruct the model on the desired behavior or approach in its response. Take a look at the following example − Principles of Well-Defined Prompts In the earlier discussion, we emphasized the significance of prompt engineering in influencing model output. Now, let”s delve into recommended practices for improving your prompts and identify some practices to avoid. Ensure Clarity Frame your sentences and instructions in a simple manner, making them easily understandable for ChatGPT. Be Concise Choose shorter prompts and sentences. Break your instructions into smaller, coherent sentences for improved understanding. Maintain Focus Ensure that the prompt centers on a clearly defined topic to avoid the risk of producing overly generic output. Consistency Maintain a consistent tone and language during the conversation for a more coherent interaction. Acting as… The technique of having ChatGPT assume the identity of someone, or something has shown remarkable effectiveness. You can streamline the information you need from the model by instructing it to “act like” the desired person or system. We’ve already seen the roleplay prompt example in the previous section, where ChatGPT acted as a detective.

ChatGPT – For Code Writing ChatGPT can serve as a versatile companion and assist developers in various coding tasks such as generating code snippets, bug fixing, code optimization, rapid prototyping, and translating code between languages. This chapter will guide you, through practical examples in Python using the OpenAI API, how ChatGPT can enhance your coding experience. Automated Code Generation Using ChatGPT We can create code snippets in any programming language effortlessly with ChatGPT. Let’s see an example where we used OpenAI API to generate a python code snippet to check if a given number is an Armstrong number or not − Example import openai # Set your OpenAI API key openai.api_key = ”your-api-key-goes-here” # Provide a prompt for code generation prompt = “Generate Python code to check if the number is an Armstrong number or not.” # Make a request to the OpenAI API for code completion response = openai.Completion.create( engine=”gpt-3.5-turbo-instruct”, prompt=prompt, max_tokens=200 ) # Extract and print the generated code from the API response generated_code = response[”choices”][0][”text”] print(generated_code) Output The above code snippet will give us the below Python code snippet that we can use to check if the given number is Armstrong number or not. num = int(input(“Enter a number: “)) sum = 0 temp = num while temp > 0: digit = temp % 10 sum += digit ** 3 temp //= 10 if num == sum: print(num, “is an Armstrong number”) else: print(num, “is not an Armstrong number”) Bug Fixing Using ChatGPT ChatGPT can help us in identifying and fixing bugs in our code. It can also provide insights to make our code error-free. To make it clear, let’s see an example below − import openai # Set your OpenAI API key openai.api_key = ”your-api-key-goes-here” # Example code with a bug code_with_bug = “for j in range(5): print(i)” # Provide a prompt to fix the bug in the code prompt = f”Fix the bug in the following Python code:n{code_with_bug}” # Make a request to the OpenAI API for bug fixing response = openai.Completion.create( engine=”gpt-3.5-turbo-instruct”, prompt=prompt, max_tokens=150 ) # Extract and print the fixed code from the API response fixed_code = response[”choices”][0][”text”] print(fixed_code) After running the above code snippet, ChatGPT will give you the below insight − The bug in the above code is that the variable used in the loop, “j”, is not being used at all in the loop body. Instead, the variable “i” is being printed, which is not declared or initialized anywhere in the code snippet. To fix this bug, we can either replace “i” with “j” in the loop body, or change the initial variable in the loop definition from “j” to “i” to ensure that the correct variable is being used in the loop. Corrected code: for i in range(5): print(i) Code Optimization Using ChatGPT We have seen ChatGPT can generate code snippets very quickly and can also identify and fix the bugs in our code. Another crucial part of the development process is code optimization which helps us improve performance and speed of any application. Example The following code shows how ChatGPT can help us optimize our code − import openai # Set your OpenAI API key openai.api_key = ”your-api-key-goes-here” # Example code to optimize code_to_optimize = “sum_of_squares = sum(i**2 for i in range(1000000))” # Provide a prompt to optimize the code prompt = f”Optimize the following Python code:n{code_to_optimize}” # Make a request to the OpenAI API for code optimization response = openai.Completion.create( engine=”gpt-3.5-turbo-instruct”, prompt=prompt, max_tokens=200 ) # Extract and print the optimized code from the API response optimized_code = response[”choices”][0][”text”] print(optimized_code) Output Here are the suggestions to optimize the above code is given by ChatGPT − One possible way to optimize this code is to use the built-in function “sum” with a list comprehension instead of a generator expression. This can be done as follows: sum_of_squares = sum([i**2 for i in range(1000000)]) Using list comprehension instead of a generator expression can reduce the time complexity of this code from O(n) to O(1). This is because list comprehension creates a list of all the squared values beforehand, while generator expressions calculate each squared value individually when needed. This makes the computation for summing the squared values much faster. Another optimization technique could be to use the built-in function “pow” instead of the exponential operator “**”. This can be done as follows: sum_of_squares = sum(pow(i,2) for i in range(1000000)) The “pow” function is optimized for calculating powers and may improve the performance of the code. Rapid Prototyping Using ChatGPT Rapid prototyping is another crucial step in the development process. It allows developers to quickly test and iterate on ideas. ChatGPT, with its ability to generate code snippets, has become a valuable tool for swift prototyping. Example In this example, we”ll explore how ChatGPT can assist in creating a Python code snippet for fetching data from a web API and printing the first 3 results. import openai # Set your OpenAI API key openai.api_key = ”your-api-key-goes-here” # Provide a prompt for rapid prototyping prompt = “Create a Python code snippet to fetch data from a web API and print the first 3 results.” # Make a request to the OpenAI API for code completion response = openai.Completion.create( engine=”gpt-3.5-turbo-instruct”, prompt=prompt, max_tokens=250 ) # Extract and print the prototyped code from the API response prototyped_code = response[”choices”][0][”text”] print(prototyped_code) Output Let’s see the response from ChatGPT − import requests # Define the URL of the web API url = “https://example.com/api” # Send a GET request and store the response response = requests.get(url) # Convert the JSON response to a Python dictionary data = response.json() # Loop through the first 3 items in the response for i in range(3): # Print the title and description of each item print(“Title:”, data[“results”][i][“title”]) print(“Description:”, data[“results”][i][“description”]) # Output: # Title: Example Title 1 # Description: This is the first example result. # Title: Example Title 2 # Description: This is the second example result. # Title: Example Title 3 # Description: This is the third example result. Code Translation

ChatGPT – Fundamentals Have you ever imagined having a digital companion that not only comprehends your words but also delivers coherent responses? If not, consider ChatGPT, as that”s precisely the function it performs! In this opening chapter, let”s have brief overview of how ChatGPT evolved and some of its popular use cases. The Evolution of ChatGPT Just 5 days after OpenAI unveiled the web preview of ChatGPT, the service notched up a staggering 1 million users! ChatGPT”s succcess ignited a worldwide surge in AI innovation. Since it first came out, OpenAI has been working hard to make ChatGPT even better. They started with a Pro version using the powerful GPT-4 model. After that, they added features like web browsing and creating images with Dall-E. Now, ChatGPT is not just for chatting; it can do much more, for example, looking at the web and making pictures. This continuous evolution underscores OpenAI”s dedication to refining and expanding the capabilities of ChatGPT to offer users a dynamic conversational AI experience. Use Cases of ChatGPT People often call ChatGPT the “do-anything-machine” because it”s great for getting lots of different jobs done. If it can”t do something, it can probably tell you how to do it. Many users find it the best choice for all sorts of tasks, making it a top pick for general work. ChatGPT has showcased impactful use cases across diverse industries. Let”s explore some of them in this section. ChatGPT for Code Writing Ever wished for a coding buddy? Developers are leveraging ChatGPT as their coding companion, utilizing its capabilities to streamline the writing, understanding, and debugging of any code. ChatGPT is becoming an essential tool in the coding process, offering valuable guidance and support throughout development tasks. Example Let’s check out an example below that generates a Python program for reversing a string − ChatGPT for Content Creation Make writing fun with ChatGPT! Creators are using ChatGPT to unlock their creative potential. Whether they are crafting stories or blogs, it assists in generating engaging content, providing inspiration, and simplifying the writing process. It also assists in summarizing the book or article. Example Let’s check out an example that writes a Facebook post description about free online courses for AI with Python − ChatGPT for Marketing Businesses are using ChatGPT to elevate their marketing strategies by creating custom marketing plans or strategies. It actively contributes to crafting ads, writing appealing content, and adapting to trends, making it a valuable ally in enhancing brand image. Example Here is an example in which we are using ChatGPT to write Google Ad headlines and descriptions for IT company − ChatGPT for Job Seekers Job seekers are turning to ChatGPT as their career coach. They are utilizing this LLM’s capabilities to craft resumes, write compelling cover letters, and prepare for interviews by answering interview questions, finding valuable support in their job search journey. Example Let’s see how we can use ChatGPT to write a cover letter for a software engineer role − ChatGPT for SEO Online content creators are using ChatGPT to enhance their SEO efforts. It actively generates SEO-friendly content, meta descriptions, and blog posts, ensuring that their online presence is easily discoverable by search engines. Example In this example, we use a prompt to generate 10 long-tail keyword ideas for a business that provides IT solutions to other businesses − ChatGPT in Healthcare Professionals in the healthcare sector are integrating ChatGPT into their workflows. They are using it for clinical decision support, medical recordkeeping, and disease surveillance, information retrieval, and as a virtual assistant, improving overall efficiency in healthcare tasks. Example Let’s see an example in which ChatGPT compare the efficacy of two different approaches for chronic pain management − ChatGPT for Customer Service Make talking to companies easier with ChatGPT! Companies are incorporating ChatGPT to simplify customer interactions. They are using it to assist with queries, provide information, and ensure a positive user experience, enhancing the quality of customer service. Example In this example, we as a Grocery Store, assisting the customer and offering solution through ChatGPT − ChatGPT for Education ChatGPT is like a study buddy! Students and educators alike are using ChatGPT as virtual tutors. It assists in explanations of complex subjects, answers questions, and makes learning interactive across various subjects, providing valuable support in educational contexts. Example Let’s see how ChatGPT summarize the life of Mahatma Gandhi for students and teachers − ChatGPT for Entertainment Writers and creators are using ChatGPT to spark their creativity. It actively contributes to generating plot ideas, video game storylines, writing movie scripts and dialogues, and creating engaging content, making it an invaluable tool in the entertainment industry. Example In the example below, ChatGPT is writing a short story having Basketball the main character − ChatGPT as Your Daily Assistant Meet your everyday helper! Individuals are relying on ChatGPT as their everyday helper to make their daily tasks seamless and enjoyable. They are using it for weather updates, setting reminders, and obtaining quick answers to queries, making exercise and diet plans. Example Let’s see how we can get some nutritious vegetarian recipes from ChatGPT − Other companies are observing the widespread popularity of ChatGPT, a part of a wave of so-called generative AI, and are exploring ways to integrate LLMs into their products and services. Microsoft, a strategic partner of OpenAI, has seamlessly integrated this technology into its core products, such as the MS 365 Suite. Bing, the search engine adopted GPT technology to rival Google’s dominance. Google has incorporated conversational AI features into its primary search product and unveiled its chatbot named Bard which is powered by LaMDA (Language Model for Dialogue Applications). We will cover these use cases in detail in the subsequent chapters. Limitations of ChatGPT As an AI language model, ChatGPT demonstrates its prowess in an array of tasks such as language translation, songwriting, research queries, and even generating computer code. This versatility positions ChatGPT as a popular tool for various applications including marketing, daily assistant tasks, healthcare support,

ChatGPT – GPT-4o (Omni) GPT-4o (Omni), the latest innovation of OpenAI, is a big step forward in generative AI. This new language model offers advanced capabilities, multimodal functionality, and improved contextual understanding. GPT-4o (Omni) is a significantly faster version of its predecessor, GPT-4. This new model will transform how we use this technology and provide us with amazing new capabilities and applications. In this chapter, we will highlight the GPT-4o language model, its availability and pricing, key features, and how it differs from GPT-4. What is OpenAI GPT-4o (Omni)? GPT-4o is the latest version of the Generative Pre-trained transformer series developed by OpenAI. This advanced language model is a step towards more natural human-computer interaction as it can understand and respond to any combination of text, audio, images, and video. GPT-4 Omni model is much faster and 50% cheaper than its successor GPT-4 Turbo. In GPT-4o, the “o” stands for “Omni” which signifies the model’s ability to accept and process “all” kinds of information from different formats including − Text − Accepting text input and processing it always being a core strength of all GPT models. This strength allows GPT-4o (Omni) model to converse, answer user’s queries, and generate creative text formats like story, code, or poems. Audio − Understanding the spoken word is a groundbreaking feature of GPT-4o. It can understand and analyze the music, or even write lyrics inspired by that music. Vision − Imagine showing GPT-4o a picture and it can analyze its content. It can also tell us a story based on that image. This multimodal capability allows GPT-4o to classify images or create captions for videos. GPT-4o (Omni) Model Availability and Pricing GPT-4o is accessible to Free tier users but with a restriction on the number of words per response. The plus users can also access the GPT-4o Omni model but with up to 5x higher word limit per response. Basic access to GPT-4o is free, but the cost for advanced tiers and API access may depend on usage and demand. Key Features of GPT-4o Some of the key features of GPT-4o are as follows − Enhanced Scale and Capacity In comparison to earlier models, GPT-4o (Omni) has a greater number of parameters which enables it to analyze and generate contextually more relevant output. This increased capacity allows GPT-4o for better handling of complex queries. Multimodal Capabilities GPT-4o is multimodal which means that it can process and generate content across various media types including text, audio, images, and video. This ability makes it a versatile tool for diverse applications, from content creation to interactive media. Improved Contextual Understanding One of the significant disadvantages of previous models was that they struggled with maintaining context in long-form content. GPT-4o got improvements and integrates advanced context-aware mechanisms which enable it to maintain context in long-form content. Fine-Tuning and Adaptability GPT-4o has fine-tuning capabilities, that’s the reason user can customize it to meet specific industry needs or personalized for individual also. This adaptability feature ensures that the model can deliver the most relevant and accurate outputs based on the context and user requirements. Ethical and Safe AI GPT-4o includes advanced safety and ethical considerations which prevents it from generating harmful content. Interactive Media Generation GPT-4o can generate and edit multimedia content, including interactive visual and audio elements. This feature is useful for creating rich, engaging media experiences. Allows to Switch Models in a Chat A new feature is added in OpenAI GPT-4o with the help of which users can switch the model in the middle of the conversation. Suppose if you want to switch to chat with another model like GPT-3.5, you can click on the sparkle button icon that appears at the end of the response as shown in the screenshot below − Support File Attachments Earlier GPT models did not support any kind of file attachments but in GPT-4o user can upload images, videos, or any file like PDF or Word to analyze it. Users can also ask any question about the content of the uploaded file. Comparison Between GPT-4 and GPT-4o (Omni) The following table presents a comparison between GPT-4 and GPT-4o based on their features − Feature GPT-4 GPT-4o (Omni) Scale and Capacity High but with substantial parameters Higher with significantly more parameters for greater capacity. Multimodal Capabilities It is primarily text-based model. It can process and generate content across various media types including text, audio, images, and video. Contextual Understanding It is improved over GPT-3.5 model. It integrates advanced context-aware mechanisms which enable it to maintain context in long-form content. Fine-Tuning and Adaptability It has robust fine-tuning capabilities. It has enhanced fine-tuning for industry specific and personalized applications. Ethical and Safety Measures It includes some basic ethical considerations. It has some advanced safety and ethical mechanisms that prevent it generating harmful content. Computational Requirements High Very high. It requires more computational resources. Training Data It needs a large and diverse dataset. It needs more diverse and larger datasets to improve versatility. Performance It can generate high-quality language output. It can generate multimodal content. Applications Mainly Text-based applications such as chatbots, content creation etc. It has wider range of applications including content creation, virtual assistants, and multimodal projects. User Interaction User interaction is primarily through text. User interaction is enhanced using various media types. Release and Availability It is an earlier version which is available free for Free tier users. It is the latest version having some advanced features. It is accessible to Free tier users but with a restriction on the number of words per response. The plus users can also access it with up to 5x higher word limit per response. Conclusion We explored the GPT-4o (Omni) model in this chapter along with its availability and pricing. We also covered some of the key features of this new language model which makes it superior to its predecessor, GPT 4. A comparison has also been made between GPT-4 and GPT-4o (Omni) models.

ChatGPT – For Content Creation Since its launch, ChatGPT has captured the attention of content creators faster than expected. In this chapter, we will see various ways to use ChatGPT for content creation. Along with that, we will also see Python implementation using OpenAI API. Get an API Key from OpenAI First of all, you”ll need to sign up on the OpenAI platform and obtain an API key. Once you have your API key, you can install the OpenAI Python library as follows − pip install openai Now, you”re ready to infuse your content creation projects with the creative capabilities of ChatGPT. Generating Text Using ChatGPT In its capacity as a language model, ChatGPT excels at crafting text in accordance with user prompts. For example, you can use ChatGPT to generate a story as below − import openai # Set your OpenAI API key openai.api_key = ”your-api-key-goes-here” # Define a prompt for text generation prompt = “Write a short story about a detective solving a mysterious case.” # Specify the OpenAI engine and make a request response = openai.Completion.create( engine=” gpt-3.5-turbo-instruct”, prompt=prompt, max_tokens=100 ) # Extract the generated text from the API response generated_text = response[”choices”][0][”text”] # Print or use the generated text as needed print(generated_text) Note − Replace “your-api-key-goes-here” with your actual OpenAI API key. The above example prompts the model to generate a short story about a detective, and you can customize the prompt and other parameters based on your specific use case. In this case, we got the following output − Detective Mark Reynolds had been on the force for over a decade. He had seen his share of mysteries and solved his fair share of cases. But the one he was currently working on was unlike any he had encountered before. Note that the system may produce a different response on your system when you use the same code with your OpenAI key. Generating Video Scripts Using ChatGPT As we know generating video content requires scripting and ChatGPT can help you in the creation of video scripts. You can utilize the generated text as a foundation for initiating your video content creation journey. Let’s check out the below example − import openai # Set your OpenAI API key openai.api_key = ”your-api-key-goes-here” # Define a prompt for generating a video script prompt = “Create a script for a promotional video showcasing our new AI product.” # Specify the OpenAI engine and make a request response = openai.Completion.create( engine=”gpt-3.5-turbo-instruct”, prompt=prompt, max_tokens=100 ) # Extract the generated script from the API response generated_script = response[”choices”][0][”text”] # Print or use the generated script as needed print(generated_script) In this case, we got the following output − [Opening shot of a modern office space with employees working at their desks] Voiceover: Are you tired of mundane tasks taking up valuable time at work? Do you wish there was a way to streamline your workflow and increase productivity? [Cut to employees looking stressed and overwhelmed] Voiceover: Well, look no further. Our company is proud to introduce our latest innovation – our revolutionary AI product. [Cut to a sleek and futuristic AI device on a desk] Music Composition Using ChatGPT ChatGPT can be used for music composition by providing it with a musical prompt or request. The generated musical ideas or lyrics can be then used as inspiration for your compositions. Here”s a simple example that demonstrates how ChatGPT can generate a short piano melody based on the given prompt − import openai # Set your OpenAI API key openai.api_key = ”your-api-key-goes-here” # Define a prompt for music composition prompt = “Compose a short piano melody in the key of C major.” # Specify the OpenAI engine and make a request response = openai.Completion.create( engine=”gpt-3.5-turbo-instruct”, prompt=prompt, max_tokens=300 ) # Extract the generated music composition from the API response generated_music = response[”choices”][0][”text”] # Print or use the generated music as needed print(generated_music) In this case, we got the following output − Here is a simple piano melody in the key of C major: C D E F G A G F E D C B A C The melody begins and ends on C, the tonic note of the C major scale. It then moves up and down the scale, primarily using steps and occasionally skipping a note up or down. This gives the melody a smooth and pleasant flow. Note that you can customize the prompt to guide the style, genre, or specific elements of the music you want to create. Generating Interactive Content Using ChatGPT You can also use ChatGPT to generate dynamic dialogues, quizzes, or choose-your-own-adventure narratives. Let’s see an example where we are using ChatGPT to generate dynamic dialogues for a school play on the topic “Robotics and society”. import openai # Set your OpenAI API key openai.api_key = ”your-api-key-goes-here” # Define a prompt for generating dialogues prompt = “Write dynamic dialogues for a school play on the topic ”Robotics and society”.” # Specify the OpenAI engine and make a request response = openai.Completion.create( engine=”gpt-3.5-turbo-instruct”, prompt=prompt, max_tokens=100 ) # Extract the generated dialogues from the API response generated_dialogues = response[”choices”][0][”text”] # Print or use the generated dialogues as needed print(generated_dialogues) The following generated text can serve as a starting point for creating engaging and dynamic dialogues for the play − (Scene opens with a group of students gathered around a table, discussing about a robotics project) Student 1: Okay everyone, let”s finalize our project idea for the robotics competition. Student 2: How about a robot that assists elderly people in their daily tasks? Student 3: That”s a great idea, but I think we can do something more impactful for society. Content Enhancement Using ChatGPT ChatGPT can be utilized for creative suggestions, enhancements, or even summarization by providing it with specific instructions to improve or expand upon existing content. In the below example the model is asked to enhance the provided content − import openai # Set your OpenAI API key openai.api_key = ”your-api-key-goes-here” # Define content that needs enhancement input_content

ChatGPT – For Marketing Read this chapter to discover how ChatGPT can help you enhance different aspects of your marketing strategy. We will explore various ChatGPT applications such as Email Automation, Ad Copywriting, Social Media Content, Chatbots, and Language Translation in the marketing realm. Along with that we will also see Python implementation of these applications using OpenAI API. Email Automation Using ChatGPT Email marketing remains a cornerstone of customer engagement. With ChatGPT, you can streamline and personalize your email automation process. Let”s look at a Python code example that generates a personalized email using the OpenAI API − import openai # Set your OpenAI API key openai.api_key = ”your-api-key-goes-here” # Define customer details customer_name = “Gaurav” customer_interest = “Herbal Handwash” # Create a prompt for email generation prompt = f”Compose a personalized email to {customer_name} highlighting the benefits of {customer_interest}.” # Specify the OpenAI engine and make a request response = openai.Completion.create( engine=”gpt-3.5-turbo-instruct”, prompt=prompt, max_tokens=200 ) # Extract the generated email from the API response generated_email = response[”choices”][0][”text”] # Print or use the generated email as needed print(generated_email) We will get the following output − Subject: Say goodbye to harsh chemicals with Herbal Handwash! Hello Gaurav, I hope this email finds you well. I am writing to introduce you to a product that has completely changed my handwashing routine – Herbal Handwash. I believe you will also find it just as amazing as I did. As you may already know, traditional hand soaps can be harsh on our skin with their strong chemicals and fragrances. But with Herbal Handwash, you can say goodbye to those worries. Made with all-natural ingredients and essential oils, this handwash is gentle and nourishing to the skin. It is free of any harsh chemicals, parabens and sulfates making it suitable for all skin types. Apart from being gentle on the skin, Herbal Handwash also leaves a subtle and refreshing fragrance on your hands. The blend of essential oils gives it a pleasant aroma which is not overpowering. Unlike other chemical-based hand soaps, you won”t have any harsh or artificial chemicals. This example demonstrates how ChatGPT can help automate the creation of personalized emails for your customers. Ad Copywriting Using ChatGPT You can use ChatGPT to craft catchy and persuasive ad copy which is crucial for marketing success. Let’s see a Python example that generates a 150 words ad for our product named ‘Hexa Pro’ − import openai # Set your OpenAI API key openai.api_key = ”your-api-key-goes-here” # Define product details product_name = “Hexa Pro” product_benefits = “cutting-edge features, unmatched performance” # Create a prompt for ad copy generation prompt = f”Create an ad copy for the new {product_name} highlighting its {product_benefits}.” # Specify the OpenAI engine and make a request response = openai.Completion.create( engine=”gpt-3.5-turbo-instruct”, prompt=prompt, max_tokens=150 ) # Extract the generated ad copy from the API response generated_ad_copy = response[”choices”][0][”text”] # Print or use the generated ad copy as needed print(generated_ad_copy) Check out the generated ad copy below − Introducing the new Hexa Pro – the ultimate machine that sets new standards in performance and innovation. Say goodbye to ordinary and hello to extraordinary with its cutting-edge features and unmatched power. Experience a new level of precision with its state-of-the-art hexagonal blades that effortlessly glide through any material. From tough fabrics to dense materials, Hexa Pro tackles it all with ease. Plus, its advanced motor delivers lightning-fast speed for seamless cuts every time. But that”s not all, Hexa Pro is designed for comfort and convenience. With its ergonomic handle and lightweight body, you can work for hours without any strain. And the convenient cordless design allows you to take it anywhere without any hassle. Chatbots and ChatGPT Chatbots have become indispensable in customer interactions. ChatGPT can help you enhance your chatbot”s conversational abilities. Here”s a Python example − import openai openai.api_key = ”your-api-key-goes-here” messages = [ {“role”: “system”, “content”: “You are an intelligent assistant.”} ] while True: message = input(“user: “) if message: messages.append( {“role”: “user”, “content”: message}, ) chat = openai.ChatCompletion.create( model=”gpt-4″, messages=messages ) reply = chat.choices[0].message.content print(f”Bot: {reply}”) messages.append({“role”: “assistant”, “content”: reply}) Upon executing the provided code, you”ll be prompted to input a query. For instance, if we use the query “I”ve purchased product X from your website and wish to initiate a replacement. Could you guide me through the process?” The code utilizes ChatGPT to generate a response, and due to the infinite while loop, it continuously seeks input from the user. user: “I”ve purchased product X from your website and wish to initiate a replacement. Could you guide me through the process?” Bot: To initiate a replacement for the product you purchased from our website, please follow these steps: 1. Contact our customer service team using the contact information provided on our website. You can usually find the contact details on the “Contact Us” page or in the order confirmation email you received. 2. Inform the customer service representative that you would like to initiate a replacement for product X. Provide them with your order number, the details of the item you purchased, and the reason for requesting a replacement. 3. Our customer service team will guide you through the replacement process, which may include returning the original product and receiving a new one, or arranging for a replacement to be shipped to you. 4. Follow any instructions provided by our team regarding returning the product, receiving the replacement, or any other necessary steps to complete the replacement process. If you have any specific questions or need further assistance during the process, please do not hesitate to reach out to our customer service team for more personalized help. Generating Social Media Content Using ChatGPT Creating compelling content on social media is essential to make your brand visible. ChatGPT can help you create engaging social media posts. Let’s have a look at a Python example in which ChatGPT creates captivating social media content on ‘National Engineers Day’ − import openai # Set your OpenAI API key openai.api_key = ”your-api-key-goes-here” #

How Does ChatGPT Work? Artificial Intelligence (AI) has become an integral part of how we live, work, and interact with the world around us. Within AI, there are several verticals such as natural language processing (NLP), computer vision, machine learning, robotics, etc. Among them, NLP has emerged as a critical area of research and development. ChatGPT, developed by OpenAI, is one of the best examples of the advancements made in NLP. Read this chapter to know how ChatGPT works, the rigorous training process it undergoes, and the mechanism behind its generation of responses. What is GPT? At the core of ChatGPT lies the powerful technology called GPT that stands for “Generative Pre-trained Transformer”. It is a type of AI language model developed by OpenAI. GPT models are designed to understand and generate natural language text almost like how humans do. The image given below summarizes the major points of GPT − Components of GPT Let’s break down each component of GPT − Generative Generative, in simple terms, refers to the model’s ability to generate new content like text, images, or music, based on the patterns it has learned from training data. In the context of GPT, it generates original text that sound like they were written by a human being. Pre-trained Pre-training involves training a model on a large dataset. In this phase, the model basically learns relationships withing the data. In case of GPT, the model is pretrained on vast amount of text from books, articles, websites, and more using unsupervised learning. This helps GPT to learn to predict the next word in a sequence. Transformer The transformer is a deep learning architecture used in the GPT model. Transformer uses a mechanism called self-attention to weigh the importance of different words in a sequence. It enables GPT to understand the relationship between words and allow it to produce more human like outputs. How ChatGPT Was Trained? ChatGPT was trained using a variant of GPT architecture. Below are the stages involved in training ChatGPT − Language Modelling ChatGPT was pre-trained on a large collection of text data from the internet, such as books, articles, websites, and social media. This phase involves training the model to predict the next word in a sequence of text given all the preceding words in the sequence. This pre-training step helps the model learn the statistical properties of natural language and develop a general understanding of human language. Fine Tuning After pre-training, ChatGPT was fine-tuned for conversational AI tasks. This phase involves further training the model on a smaller dataset having data such as dialogue transcripts or chat logs. During fine-tuning, the model uses techniques such as transfer learning to learn generating contextually relevant responses to the user queries. Iterative Improvement During the training process, the model is evaluated on various metrics like response coherence, relevance, and fluency. Based on these evaluations, to improve the performance the training process may be iterated. How Does ChatGPT Generate Responses? ChatGPT’s response generation process uses components like neural network architecture, attention mechanism, and probabilistic modeling. With the help of these components, ChatGPT can generate contextually relevant and quick responses to the users. Let’s understand the steps involved in ChatGPT’s response generation process − Encoding The response generation process of ChatGPT starts with encoding the input text into a numerical format so that the model can process it. This step converts the words or subwords into embeddings using which the model captures semantic information about the user input. Language Understanding The encoded input text is now fed into the pre-trained ChatGPT model that further process the text through multiple layers of transformer blocks. As discussed earlier, the transformer blocks use self-attention mechanism to weigh the importance of each token in relation to the others. This helps the model to contextually understand the input. Probability Distribution After preprocessing the input text, ChatGPT now generates a probability distribution over the vocabulary for the next word in the sequence. This probability distribution has each word being the next in sequence, given all the preceding words. Sampling Finally, ChatGPT uses this probability distribution to select the next word. This word is then added to the generated response. This response generation process continues until a predefined stopping condition is met or generates an end-of-sequence token. Conclusion In this chapter, we first explained the foundation of ChatGPT which is an AI language model called Generative Pre-trained Transformer (GPT). We then explained the training process of ChatGPT. Language modelling, fine tuning, and iterative improvements are the stages involved in its training process. We also discussed briefly how ChatGPT generates contextually relevant and quick responses. It involves encoding, language understanding, probability distribution and sampling, which we discussed in detail. ChatGPT, through its integration of the GPT architecture, rigorous training process, and advanced response generation mechanisms, represents a significant advancement in AIdriven conversational agents.

ChatGPT – Competitors Over the years, various iterations of ChatGPT have been developed, each bringing improvements and additional features to the table. The key versions include − GPT-1 − Introduced in 2018, GPT-1 served as the inaugural model in the GPT series, focusing on text generation. GPT-2 − Unveiled in 2019, GPT-2 elevated the game with 1.5 billion parameters. It garnered attention for its highly persuasive text generation capabilities, albeit sparking concerns about disinformation. GPT-3 − Launched in 2020, GPT-3 stands as the most recent and advanced version in the GPT series, boasting 175 billion parameters. Applauded for its heightened ability to generate more natural text and perform diverse natural language processing tasks. GPT-4 − Unveiled in 2023, OpenAI claims that “GPT-4 can solve challenging problems with greater accuracy, thanks to its broader general knowledge and advanced reasoning capabilities.” Each iteration has played a crucial role in elevating the quality and precision of automated text generation, facilitating more natural and seamless communication between chatbots and users. In fact, ChatGPT”s capabilities are expected to undergo significant evolution in the coming years, especially with OpenAI actively working on the next-generation GPT-5 language model. Competitors of ChatGPT While ChatGPT holds a prominent position, various competitors, including Google, Meta, Anthropic, and Amazon, are using Large Language Models (LLMs), deep learning, and fine-tuning to establish their dominance in the market. Within the AI Large Language Model (LLM) domain, there are several promising competitors to ChatGPT. In this chapter, we will explore each of these alternatives to ChatGPT individually. Google Gemini (Formerly Google Bard) Google Gemini is a formidable competitor to ChatGPT. It made its debut in March 2023 as a conversational AI chatbot employing machine learning (ML), natural language processing (NLP), and generative AI to comprehend user prompts and furnish text responses. In contrast to ChatGPT, Gemini has the unique capability to access the Internet, integrating information scraped from recently published content into its responses. Initially trained on Google LaMDA, a large language model (LLM), Gemini underwent a transformative re-training in May 2023, transitioning to the more advanced Pathways Language Model 2 (PaLM 2). Google asserts that PaLM 2 processes information up to 500 times faster than LaMDA and achieves a remarkable tenfold increase in accuracy. Google rebranded the Bard chatbot as Gemini on February 8, 2024. Midjourney Midjourney is an innovative AI tool, specializing in swiftly transforming prompts into images. With its monthly model updates, Midjourney continues to push the boundaries of creative AI. As Midjourney operated on a self-funded and closed-source basis, its intricate workings remain undisclosed. The platform employs machine learning technologies, incorporating large language and diffusion models. In contrast to counterparts like ChatGPT and Bing Chat, Midjourney adopts a unique approach with a subscription-based model, requiring graphics processing units (GPUs) for optimal performance. While lacking a free trial, the basic plan, priced at $10, facilitates the generation of over 200 images upon command. Claude 2 In July 2023, Anthropic, an AI company, unveiled its latest chatbot named Claude 2 which is powered by a large language model. Claude 2 represents a notable upgrade from Anthropic”s previous AI iteration, Claude 1.3. Noteworthy improvements include enhanced code-writing capabilities based on written instructions and an expanded “context window.” Users can now input entire books and pose questions to Claude 2 based on their content. These enhancements position Claude 2 on par with leading models like GPT-3.5 and GPT-4, which drive OpenAI”s ChatGPT. To meet claude 2, sign up at https://claude.ai/. Runway ML Runway ML represents a groundbreaking platform designed for artists, designers, and creators to use the potential of machine learning. Runway ML empowers users to craft videos using text prompts, alter video styles using text or images, and craft personalized portraits, animals, styles, and beyond. Simple and accessible, Runway ML eliminates the necessity for in-depth programming knowledge. A standout feature of Runway ML lies in its AI Magic Tools, which facilitate real-time video editing, collaboration, and a myriad of other functionalities. The creative potential with Runway ML”s AI Magic Tools is boundless, offering a diverse range of possibilities. GitHub Copilot GitHub Copilot, introduced in 2021, is a revolutionary coding assistant developed by GitHub in collaboration with OpenAI. Seamlessly integrated into popular code editors, Copilot offers developers real-time code suggestions and autocompletions. Powered by OpenAI”s Codex, it draws insights from a vast array of public code repositories, providing context-aware code snippets to enhance coding efficiency. Copilot transforms the development landscape by assisting developers with intelligent code generation, fostering productivity, and making coding more accessible. Unlike ChatGPT, which focuses on natural language conversations, GitHub Copilot is uniquely tailored to expedite code production. Perplexity AI Perplexity AI, released in August 2022, functions as an AI chatbot with search engine capabilities. It is built upon GPT-3 and GPT-4 that employ sophisticated technologies like natural language processing (NLP) and machine learning. This enables the platform to deliver precise and thorough responses to user queries. Tailored for real-time web searches, Perplexity AI ensures access to the latest information across diverse topics. Fueled by robust language models, particularly OpenAI”s GPT technology, the platform excels in comprehending and generating human-like text. Positioned as an answer engine, Perplexity AI strives to enhance the way individuals explore and exchange information. Perplexity AI is conveniently accessible to a diverse user base, with both web and iPhone app versions available. Users can freely utilize Perplexity AI by visiting their website. Follow these steps to engage with Perplexity AI − Navigate to . Pose your question by entering it into the search bar and clicking the blue arrow. Evaluate Perplexity AI”s response along with the provided sources. Continue the interaction by asking follow-up questions using the “Ask a follow-up” bar below. Meta Llama 2 Llama 2 is Meta’s large language model (LLM) that can generate text and code while optimizing computing power and resources. Llama 2 achieves a Massive Multitask Language Understanding (MMLU) score of 68.9, just slightly trailing GPT 3.5”s 70.0. While it falls short of GPT-4”s 86.4 rating, this proximity establishes Llama 2

ChatGPT – For SEO SEO stands for Search Engine Optimization. It”s a set of practices and strategies that website owners and marketers use to improve the visibility of a website on search engines like Google, Bing, or Yahoo. It offers a range of tools such as keyword research and analysis, automation and reporting, optimizing content and many more to boost your website”s visibility. In this chapter, we”ll explore how ChatGPT can revolutionize your approach to SEO. This chapter will also give you practical examples, using the power of ChatGPT and the OpenAI API, to elevate your SEO strategies. Keyword Research and Analysis Using ChatGPT Keyword research and analysis are crucial components of Search Engine Optimization (SEO). They involve identifying the words and phrases (keywords) that users are likely to type into search engines when looking for information. Example Given below is an example of using ChatGPT for keyword research and analysis − import openai # Set your OpenAI API key openai.api_key = ”your-api-key-goes-here” # Prompt for keyword research prompt = “Suggest top keywords for ”healthy recipes”.” # Request keywords from ChatGPT response = openai.Completion.create( engine=”gpt-3.5-turbo-instruct”, prompt=prompt, max_tokens=50 ) # Extract and display suggested keywords suggested_keywords = response[”choices”][0][”text”] print(“Suggested keywords:”, suggested_keywords) Output Let’s check out the suggested keywords provided by ChatGPT − Suggested keywords: 1. Nutrition 2. Low-fat 3. Clean eating 4. Vegetarian 5. Plant-based 6. Gluten-free 7. Keto 8. Whole30 9. Meal prep 10. Budget-friendly NLP in SEO Natural Language Processing (NLP) techniques in SEO can improve content quality, user experience, and overall search engine visibility. Example Here is an example on using ChatGPT for NLP in SEO − import openai # Set your OpenAI API key openai.api_key = ”your-api-key-goes-here” # Prompt for NLP-based SEO analysis prompt = “Analyze the sentiment of customer reviews for ”best laptops”.” # Request sentiment analysis from ChatGPT response = openai.Completion.create( engine=”gpt-3.5-turbo-instruct”, prompt=prompt, max_tokens=150 ) # Extract and display sentiment analysis results sentiment_analysis = response[”choices”][0][”text”] print(“Sentiment analysis:”, sentiment_analysis) Output The sentiment analysis given by ChatGPT is as follows − Sentiment analysis: The sentiment of customer reviews for “best laptops” appears to be overwhelmingly positive. Many customers praise the performance, speed, and durability of the laptops, with terms such as “amazing”, “incredible”, and “fantastic” being commonly used. Multiple reviewers also mention the value for money of these laptops, with comments such as “great price” and “affordable” indicating satisfaction with the price point. Some customers also express appreciation for the design and aesthetics of the laptops, describing them as “sleek” and “stylish”. Overall, the sentiment of customer reviews for “best laptops” is highly positive, with a strong emphasis on quality, performance, and value. User Experience (UX) and SEO Using ChatGPT Search engines consider User Experience (UX) as a ranking factor, and hence improving UX is not only beneficial for website visitors but also plays a crucial role in Search Engine Optimization (SEO). Example The following example shows how you can use ChatGPT for user experience and SEO − import openai # Set your OpenAI API key openai.api_key = ”your-api-key-goes-here” # Prompt for NLP-based SEO analysis prompt = “Analyze the sentiment of customer reviews for ”best laptops”.” # Request sentiment analysis from ChatGPT response = openai.Completion.create( engine=”gpt-3.5-turbo-instruct”, prompt=prompt, max_tokens=150 ) # Extract and display sentiment analysis results sentiment_analysis = response[”choices”][0][”text”] print(“Sentiment analysis:”, sentiment_analysis) Output Here are the user experience suggestions provided by ChatGPT − User experience suggestions: 1. Streamline Navigation: The navigation of an e-commerce website should be intuitive and easy to use. It should have clear categories and subcategories to help users find what they are looking for quickly. The search bar should also be prominent and easily accessible. 2. Use High-Quality Images: High-quality images are crucial for an e-commerce website as they give customers a better understanding of the products. Use multiple product images from different angles and allow users to zoom in for a closer look. 3. Provide Detailed Product Descriptions: Along with images, a detailed product description is essential for customers to make informed decisions. It should include size, materials, features, and any other relevant information. Image and Video SEO Using ChatGPT Optimizing multimedia content i.e., images and videos, is essential for enhancing the overall SEO strategy and attracting more organic traffic. Example The following example shows how you can get some tips from ChatGPT for optimizing images and videos − import openai # Set your OpenAI API key openai.api_key = ”your-api-key-goes-here” # Prompt for image and video SEO tips prompt = “Provide tips for optimizing images and videos for better SEO performance.” # Request SEO tips from ChatGPT response = openai.Completion.create( engine=”gpt-3.5-turbo-instruct”, prompt=prompt, max_tokens=200 ) # Extract and display SEO tips for images and videos seo_tips = response[”choices”][0][”text”] print(“SEO tips for images and videos:”, seo_tips) Output Here are the tips given by ChatGPT − SEO tips for images and videos: 1. Use relevant and descriptive file names: Before uploading an image or video, give it a relevant and descriptive name that includes relevant keywords. This will help search engines understand the content of the media. 2. Optimize alt text: Alt text is used to describe an image or video for visually impaired users and search engine crawlers. Use descriptive and keyword-rich alt text to help search engines understand the content of the media. 3. Compress images and videos: Large and heavy media files can slow down your website’s loading speed, which can negatively affect your SEO. Compress your images and videos without compromising on quality to improve your website’s loading speed. SEO Automation and Reporting Using ChatGPT Automation and reporting play a vital role in the field of SEO. It involves streamlining repetitive tasks and generating insightful reports to monitor. Example Here is an example on how to use ChatGPT for SEO automation and reporting − import openai # Set your OpenAI API key openai.api_key = ”your-api-key-goes-here” # Prompt for SEO automation and reporting prompt = “Automate keyword tracking and generate an SEO report for the past month.” # Request automation and reporting from ChatGPT response = openai.Completion.create( engine=”gpt-3.5-turbo-instruct”, prompt=prompt, max_tokens=150 )