Generative AI Full Course 2026 | Gen AI Tutorial for Beginners | Gen AI Explained | Simplilearn

Hey everyone, welcome to this generative AIL course by simply. So whether you're new to AI or want to take your skills to the next level, you are in the right place. So in this course, we will start with the basics of generative AI and show you a clear learning path to help you understand this exciting technology. Learn about important AI tools like large language models, deep learning and agentic AI all explained in simple terms. We'll also dive into some powerful tools like open AI codeex, hugging face, slang chain, and deepseat and guide you through tutorials on machine learning, reinforcement learning, and AI workflows that anyone can follow. Along the way, you'll get hands-on experience with popular AI tools, learn about transformers and GANs, and also discover how AI can help you if you don't know coding through no code development. And towards the end, we'll explore advanced topics like AI benchmarking, quantum AI, and other cuttingedge innovations. Plus, we will help you get ready for job interviews with special AI tools designed to give you an edge. So, let's get started for the comments. If you're interested to master the future of technology, then the professional certificate course in generative AI and machine learning is the perfect opportunity for you. Offered in collaboration with the ENIC Academy, IT Kpur, this 11-month live and interactive program will provide you hands-on expertise in cutting edge areas like generative AI, machine learning and tools like chargu and even hugging face. You'll also gain practical experience through 15 plus projects, integrated labs, and live master classes delivered by esteemed IT Kpur faculty. Alongside, you'll earn a prestigious certificate from IIT Kpur. And you'll also receive official Microsoft badges for Azure AI courses and career support through Simply Learn's job assist program. So, hurry up and enroll now. Find the course link in the description box below and in the pin comments. Hello all, welcome to this course on generative AI for everyone. So let's start with what is generative AI. Generative AI is a type of artificial intelligence that focuses on creating new content rather than just analyzing or recognizing patterns in existing data. So unlike traditional AI which might be used for tasks like predicting outcomes or classifying data, generative AI can generate entirely new items such as text, images, music, and even videos that did not exist before. So at the heart of generative AI is the ability to learn from data sets. The AI is trained on massive amounts of data. This could be millions of images, articles, or pieces of music. So through this training, the AI learns patterns and structures within the data and then it uses this knowledge to create something new. For example, if you ask a generative AI to write a poem, it will use what it has learned about how poems are structured such as rhyme, meter, and themes to generate a brand new poem from scratch. So the core technology behind generative AI often involves advanced machine learning models, particularly GS, which stands for generative adversarial networks and transformers. So these models learn how to create realistic and relevant content by processing vast amounts of data and understanding the underlying patterns. For example, in g two parts of the model are there. One generates content and the other evaluates its authenticity. So these work together in a sort of competition which pushes a system to improve its ability to create convincing and realistic outputs over time. So generative AI is incredibly powerful because it can be used for many practical applications. It helps automate tasks that were once very time consuming like content creation or design. And it also allows for innovation in areas like entertainment, marketing and healthcare. So for example, AI can be used to create realistic video game environments or come up with product designs for business. In marketing, AI can generate personalized email campaigns or social media content at scale. However, as impressive as generative AI is, it also raises important ethical concerns. One of the major challenges is its potential for misuse such as creating deep fakes which are hyperrealistic fake videos or generating misleading information and another issue is bias. So if the AI is trained on biased or incomplete data, the content it generates could perpetuate those biases. Additionally, there's a question of job displacement. As AI generated content becomes more common in creative fields like writing or art. We'll look more about these in the following chapters. So now let's see how does Genai work. So generative AI works by using sophisticated machine learning techniques as already discussed before. So the key concept behind generative AI is that it doesn't just analyze data but rather it creates something new that mimics the patterns it has been trained on. So the technical foundation of generative AI is built in complex algorithms and models with GM and transformers being the most commonly used techniques. So let me just break it down in more detail. The first step is of course training phase which is learning from data. So generative AI models are trained on large data sets that contain examples of the type of content the model is expected to generate. So for instance, if you're training a model to generate images of cats, the model would be shown thousands or even millions of images of cats so that it can learn the various features that make up a cat like shapes, colors, textures and so on. So this process involves a training algorithm which adjusts the model's internal parameters to minimize the difference between the generated content and the real data. So essentially the model starts by making random guesses and gradually improves over time as it learns more about the data it's being trained on. The process of training can be computationally expensive often requiring powerful hardware like GPUs to process large data sets quickly. So now let's move on to the key models in generative AI. So number one is GS which stands for generative adversarial networks. So GNS are a popular architecture used in generative AI. GNS consist of two neural networks the generator and the discriminator. So the generator's job is to create new data like images, videos or text while the discriminator's job is to evaluate whether the generated data looks real or fake. So here's how it works. So the generator starts by creating random content, for example, an image. So it doesn't know what a real cat looks like at first. So it starts with something that could be completely random. And then comes the discriminator's part. The discriminator then compares the generated content to real content from the training data like some real image of the cat. And then it tries to figure out if the content is real or fake. And then the generator learns from the discriminator's feedback. Basically, the discriminator will give the feedback if the content generated is correct or not. And then the generator learns from the discriminator's feedback and tries to improve. And over time, the generator gets better at creating realistic content. And the discriminator gets better at detecting fake content. So this back and forth trading process improves the overall ability of the model to create content that looks and feels real. So, G have been used to create realistic images, generate new fashion designs and even create humanlike faces in photos. And then comes transformers. So, transformers are a different architecture used primarily in natural language processing NLP for tasks like text generation, translation and summarization. So, the transformer model is known for its ability to handle sequential data like text where the order of the data matters. So the main building blocks of transformers are attention mechanisms. The attention mechanism allows the model to focus on different parts of the input sequence like words in a sentence when making predictions. So for example, when generating a sentence, the model pays more attention to certain words in the context of the sentence to ensure the output makes sense. So here also we have two parts. First is the self attention. So this is a key concept in transformer self attention. It enables the model to consider all parts of the input data when making predictions rather than just focusing on a fixed window of data. So this helps the model to understand the relationships between words that might be far apart in a sentence. And then we have the encoder decoder structure. So in many transformer models like bird or GPT, there is a two-part structure. The encoder reads and understands the input while the decoder generates the output. So the encoder takes a whole sentence and creates a representation of it which is then passed to the decoder to generate the corresponding output. So one of the most well-known transformer based models is the famous GPT generative pre-trained transformer which powers tools like chart GPT. So GPT generates humanlike text by predicting the next word in a sequence based on the words that came before it. So it can produce anything from short answers to entire essays by learning from vast amounts of text data it is provided with. Now once the training phase is done the next phase is the data generation phase which is creating new content. So the data generation phase is where the trained generative AI model uses the knowledge it has gained from training to create new content. So this uh phase involves several steps and the type of content it generates can vary significantly depending on the model and the input provided. The first step is input to the model. So the model needs some form of input to begin generating new content. So depending on the type of model there could be various types of inputs. The first being the text generation. So in text generation for example like GPT the input is usually a prompt or a starting sentence. So for example, if you give a prompt like write a poem about the ocean, the model will use that as a basis for generating the poem. And next we have image generation. So for image generation, for example, in Dali or GM, the input might be a textual description of what the image should look like like what image what kind of image you want to generate. For example, it could be something like a sunset over the mountains with a clear sky. So in G the input could also be a random noise vector which the generator uses to create a detailed image. And then we have music generation like music where the input might be a short melody or a genre specific like write a jazz song and the AI will then produce a full musical composition in that time. So once the input is given to the model for data generation the next step is the transformation of input to output. So after receiving the input, the AI's job is to transform it into an output that aligns with the patterns and the structures it learned during the training phase. So the model uses its internal learned parameters to guide this transformation. So for example, in text generation, the model processes the input sentence or prompt and generates subsequent words one after another. So based on probabilities it has learned, it predicts the next word in the sequence considering the words before it. For example, we give input once upon a time. So the model might predict the next word could be there. For example, once upon a time there was like that leading to a coran sentence. So for example, it would produce something like once upon a time there was a dragon. So it continues this process until the text is complete. And then for image generation, if the input is a description, the AI will generate an image that fits that description. The generator uses what it has learned from textures, shapes, colors, and other image features from the training data. So for example, if the description was a cat wearing a top hat, the AI would combine the learned features of cats and hats to create an image that matches the description. Then for music generation, the model processes the given input, such as a short melody or a genre specification and uses patterns learned from training on music data to create a complete musical piece. It may follow certain patterns like rhyme, melody, and chord progression that are typical for that genre. is use of randomness. So one interesting aspect of generative AI is a use of randomness. In many cases the model starts with a random noise vector or some random elements as a starting point. In GS for example the generator begins by producing random content like a random image and refineses it through feedback from the discriminator as already discussed before. So over time as a generator improves the output becomes more coherent and realistic. So in text generation models like GPT randomness is often introduced through a sampling mechanism. The model doesn't always generate the same output for the same input. Right? As you might have seen in GPT like using chart GPT whenever you ask the same question multiple times. It provides a slightly different answer. So the model doesn't always generate the same output for the same input. There's a degree of randomness in choosing the next word which leads to more varied and creative responses. And then comes iteration and refinement. So in some cases especially with models like GM the data generation process is iterated the generator produces an initial version of the content and then it is refined over multiple cycles. So each cycle uses feedback from other components of the model to improve the output. So over time the generated content becomes closer to what humans would expect in terms of quality and realism. So once the training phase and the data generation phase is over the next phase is of course the evaluation phase which is assessing and improving the generated content. So once the content has been generated we have to evaluate it. So this phase involves checking whether the generated content meets certain criteria such as realism, relevance, coherence and creativity. So the evaluation phase can be done through both automated and human-driven processes and it can occur in real time or in a postgeneration step. So first we'll discuss about automated evaluation. So in many generative VI systems, evaluation is built directly into the model and this is particularly common in systems like GM where the discriminator serves as an evaluator. So here's how it works. First is the discriminator and GN. So after the generator produces content for example an image, the discriminator evaluates whether the generated content is real or fake. It does this by comparing the generated content to the real examples from the training data set that we had provided during the training phase. Now the discriminator gives feedback to the generator guiding it to improve its content. So over time as the generator gets better the discriminator gets better at detecting fake content also. So this creates a feedback loop where both the generator and discriminator improve in tandem. So eventually the generator creates content that is highly realistic and difficult for the discriminator to distinguish from real data. And then we have the self assessment and transformers. So in models like GPD for example, evaluation often happens internally through a process called self attention. So the model checks how well each part of the generated output fits with the surrounding context. So the model detects inconsistencies like strange word choices or unnatural phrasing. it will adjust its output to improve coherence. So after the automated evaluation we have the uh human evaluation. So while automated evaluation is crucial, human evaluation is often needed to ensure that the generated content meets higher level expectations, right? Especially when it is a complex or creative task. So for example, it's basically used for quality control. So when generative AI is used to create marketing content or writing, human editors may review the AI generated text for things like tone, accuracy and creativity. So even if the AI creates grammatically correct data or a correct content, a human might evaluate whether the text fits the brand's words or whether it resonates with the target audience and also it has to be checked for bias and ethics. So humans also play an important role in assessing whether the generated content is biased or unethical. So for instance, if an AI model is generating text or images based on biased data, human reviewers can catch these issues and make adjustments. So after the human evaluation comes fine-tuning. So once the content is evaluated, the AI model may undergo fine-tuning to further improve its output. So finetuning involves making small adjustments to the model based on feedback from the evaluation process. So for example, if a text generation model is consistently producing relevant or off-topic content, fine-tuning can involve retraining the model on more relevant data or adjusting the model's parameters to make it focus on most important aspects of the prompt. So for example, in the case of G, the feedback from the discriminator helps refine the generator's output. So the model can be trained for additional iterations to produce more realistic and high quality content. And then we have the real world testing. So once a model is generated content that meets evaluation standards, the real world testing is important. So this could involve testing the generated content in the context it's meant for such as seeing how AI generated music fits with a film or how AI generated product descriptions perform in marketing campaigns. So real world testing helps validate whether the AI generated content truly works in its intended use case or not. So now that we have a complete idea about how generative AI works and what are the steps involved, let's see some of the geni applications. So generative AI has found applications in a wide range of fields. Transforming industries and creating new possibilities across different domains. Its ability to generate novel content, simulate scenarios and automate creative task is reshaping many areas of work. So let's explore the various categories of generative AI applications and dive deeper into each of them. So the first one is of course content creation. So generative AI is revolutionizing content creation enabling the generation of articles, videos, images, music and even entire books. So let's see how the first one is text generation. One of the most popular applications of Genai is in text generation where models like GPT are used to create humanlike text. So AI tools such as chat GPT, Jasper and Copy AI help in creating or writing blog posts, social media content, advertising copy and even books. These tools can assist writers by suggesting ideas, creating outlines or generating full paragraphs based on a given prompt. They also help reduce time and effort in content creation by automating repetitive or less creative tasks. Next, we have image generation. So models like Dali and Mjourney are used to generate images based on textual descriptions. So for example, you could type a futuristic city with flying cars and AI would generate a detailed image based on that description. So this is wide applications in graphic design, advertising and video game development where unique visual content is often needed. So these models are also used to create synthetic images for trading other AI models or to generate art that would otherwise require a professional artist. And then we have video creation. So generative AI can also create videos from entire animations to deep fake videos. AI tools like runway, ML and Sincere are used to generate video content by creating realistic avatars that can speak in any language and even simulate real world applications. So this has applications in marketing, customer support and education. And then we have music generation or audio generation. So AI can compose original music tracks or generate audio effects. Tools like musicet which is from open AI and Apple Music can create entire music compositions based on genre, mood or specific instrument preferences. So this is especially useful for advertisements, film scoring or even background music in apps and video games. So AI is also being used to generate realistic voices and sound effects for example in virtual assistants or audio books. So after content creation, the next we have is healthcare. So GNI is providing or is being a gamecher in the field of healthcare for example in drug discovery and design. One of the most promising applications of Genai in healthcare is its ability to help design new molecules for drug discovery. So by analyzing known compounds and understanding their chemical structures, AI can generate new molecular structures that are likely to be effective against certain diseases. So for example, atom vice and insilic medicine use generative models to propose new drug compounds accelerating the traditionally slow and costly drug discovery process. And then in healthcare we have the medical imaging. So GIA is also being used in medical imaging to enhance the quality of images, detect abnormalities or even generate synthetic images for training purposes. So models like deep minds alpha fold have revolutionized the understanding of putting fold in which is crucial for drug design. AI tools can also assist in generating synthetic MRI scans, CT scans or X-ray images helping researchers and doctors have more data for analyzing without the need for new scans. And then we have the personalized healthcare which is very popular nowadays. So in personalized healthcare, Genai can help create personalized treatment plans based on a patient's unique health data. It can also predict how patients might respond to different treatments enabling doctors to make more informed decisions about care. And then we have the field of entertainment and media where we have the film and animation. So gen AI is used in film production to create visual effects, generate character designs and even script writing. So AI powered animation tools can generate facial expressions, voices and movements for characters. So for example, deep fake technology has been used to create realistic simulations of actors performance which we see nowadays in social media allowing filmmakers to reuse older footage or generate entirely new scenes with minimal resources. And then in entertainment and media we have game development. So for example in video games generative AI helps in generating vast dynamic environments and creating new characters, plots or levels. So for example, games like No Man's Sky have used procedural generation powered by AI to create millions or unique planets each with different ecosystems and environments. And then again what we as discussed before we have music and soundtracks. So beyond just generating individual songs, AI can also create soundtracks for movies and video games. And then we have business and marketing. So Genai is enhancing business operations. For example, Genai can be used in creating highly personalized marketing content for specific uh customer segments. It can automatically generate email campaigns, blog posts, social media content, and even custom advertisements tailored to a customer's preference. And then there are chat bots and virtual assistants. So AI powered chat bots such as chat GPT or dialog flow are becoming more intelligent in handling customer queries. So these systems are not only used for answering frequently asked questions but also for generating contextually appropriate responses assisting with lead generation and even crafting sales pitches tailored to specific outcomes. And then we have the product and service design. So in product development, Genai can help design new products or services by analyzing consumer feedback, market trends and existing products. And then we have education and training. So, Genji is transforming the education sector nowadays where we have personalized training. So, AI can create customized learning paths based on a students progress, strengths and weaknesses. So, for example, a geni system could adapt lessons, quizzes or explanations based on learners and understanding. And then we have content creation for learning. So, Genai can automatically create educational materials such as worksheets, flashcards, quizzes, and even study guides based on existing content. And finally we have the synthetic data generation. So in many fields generating synthetic data is a crucial application of genuine especially when real data is difficult to obtain or too costly to generate. So by creating realistic artificial data sets AI can simulate real world scenarios for research training or development processes. So the first one is data augmentation for AI models. GI is used to generate synthetic data that augments real world data sets especially in computer vision or natural language processing. So for example AI can generate images of objects or scenes that are under represented in existing data sets helping improve the performance of machine learning models. And then we have the privacy preserving data. So for example in sectors like healthcare or finance generating synthetic data can help create data sets that protect the personal privacy of the patients. So these data sets can be used to train AI models without exposing the sensitive information such as medical records or financial transactions. So now that you've got a clear picture of what is generative AI and how it works. Now let's go into a deeper understanding how generative AI creates content. Hi all. So in this chapter we'll see how generative AI creates content. So AI models particularly those in the domain of NLP learn and generate text by using deep learning techniques primarily through the use of neural networks. So let's explore the technical details of how AI models like GPT and similar architectures learn and generate text. So the first step is pre-processing and tokenization. So before an AI model can start learning from text data, the text, the data that we give must be first pre-processed and tokenized. So here's how it works. First, we have the text prep-processing step. So here the text data is typically cleaned and formatted to remove noise like punctuation, special characters or irrelevant words. So text may be normalized by converting everything to lower case, removing stop words example the and or correcting spelling errors. So the goal is to prepare the data for efficient processing by the AI model. And then comes tokenization. So tokenization is a process of converting text into smaller units called tokens. So tokens can be words, subwords or even characters. So for example, the sentence AI model learn and generate text might be tokenized into words like separate words as tokens like AI models learn as separate tokens. So for example here you can see this is a sample text. So here they have tokenized each word but can also tokenize each uh you know letters or a group of letters. So for example if it's generating the word is generating it can also tokenize it as gem one token e rer one token and aing is another token. So this allows the model to understand and generate less common or compound words that isn't seen before. So the next step comes training the AI model. So the learning process of AI models is typically based on deep learning a sub field of machine learning where models are trained using neural networks. So here's how it works. First let's talk about neural networks. So these networks consist of layers of interconnected nodes or neurons that mimic the structure of the human brain. So the most common neural network for NLP tasks are feed forward networks, RRM and transformers. So transformer based uh models such as GPT have become the state-of-the-art architecture for NLP due to their superior performance in handling sequential data like text. Then we have training with large data sets. So to train the model, vast amounts of data, text data are used. So these data sets are typically scrabbed from books, websites, articles and other written content. So the model learns by analyzing the context in which words appear, understanding the relationships between them and developing a general understanding of language. And then we have the objective of training. So the core objective of training a text generation model is to minimize loss function which measures how far off the model's predictions are from the actual data. So the most common loss function for text generation tasks is cross entropy loss which evaluates how accurately the model predicts the next word in a sequence of words. So during this training process the model is given a sequence of words or tokens and is asked to predict the next word in that sequence. So for instance in the sentence a cat is on the dash the model would be trained to predict words like mat roof or similar words based on the context. So the model adjusts internal parameters through a process called back propagation which allows it to learn from its mistakes and improve over time. So next comes transformers and attention mechanism. So the transformer architecture is particularly important in modern text generation models. It relies heavily on a mechanism called self attention. We have already discussed about it slightly before. Now we'll see it in more detail. So we'll just see how it works. So the first step is self attention. So in a sequence of words, not all words are equally important for understanding the meaning of other words. So the attention mechanism allows the model to weigh each word's importance in relation to others. So for instance, in a sentence, if there's a sentence, the quick brown fox jumps over the lazy dog. So the model needs to focus on fox and jumps when predicting the next word after the quick brown fox. Then comes the attention score. So the attention scores are basically calculated using a mathematical operation that compares each word or each tokens in the sequence to other words. So the result is a set of attention weights that tells the model which part of the sentence to attend to when making predictions. And then we have the multi head attention. So the transformer models use multi head attention which involves running the attention mechanism in parallel over different parts of the input allowing the model to focus on different relationships between words simultaneously. So these helps the model capture a variety of linguistic patterns such as long range dependencies and then there is position and coding. So unlike traditional sequential models like RNNs, transformers don't process words in order meaning they don't inherently capture the sequential nature of language. So to account for word order, transformers use positional encoding which adds information about the position of each word in the sequence. So this ensures that the model understands the relative positions of words in a sequence which is crucial for tasks like generating coherent sentences. Now the next step is generating the text. So once the model is trained, it can be used to generate text. So here's the process. The first one is of course input prompt. So text generation begins with an input that we give which is a seed or a starting point for the model. So this could be anything from a simple uh as a single word or a short phrase. Next is the prediction process. So the model generates text token by token. So it predicts the most probable word the most probable next token based on the context provided by the input and the previous tokens it has already generated. And then there are sampling methods. So there are different methods to sample the next token. So one of it is BV search. So the simplest approach is to always put the most likely next to word. So this can produce fluent text but may lack creativity or variation. And then we have samplings like top K sampling where instead of picking the single most likely word, the model samples from the top K most likely word. And then there is also temperature sampling where this method edges the probability distribution of the next word based on a temperature parameter. So higher temperature leads to most random selections while a lower temperature makes the model more deterministic and focused on most likely words. And then that is contextual understanding in generating text. So as a model generates text, it uses the context of the previously generated tokens to inform the next predictions. So this is especially important for maintaining consistency and logic in the generated text. So for instance, the model might need to keep track of a specific character's name or a story setting to ensure that the output, the tone of the output or the style of the output remains relevant. So once the text is generated, we have fine-tuning and transfer learning as the next steps. So first let's talk about fine-tuning. So after the model has been pre-trained on a large data set, it can be fine-tuned on more specific data to improve its performance on particular tasks. So for example, if a model is trained to generate general text, it can be fine-tuned on a data set of legal documents, medical literature or product reviews to make it more specialized. And then we have transfer learning. So transfer learning means it's a key feature especially modern NLP models. So a model trained on one task or domain. Example, for example, let's take general text generation. It can be fine-tuned and adapted to perform well on different tasks with less data. So this helps make generative AI models more adaptable and efficient. So now that you have seen how generative AI creates content, let's talk about LLMs or large language models. So LLMs have become one of the most powerful and transformative technologies in the field of AI. They leverage mass amounts of text data and deep learning techniques to generate, understand and process human language. So let's explore the technical details behind the power of LLMs and understand why they are so influential in the AI world. So first let's see what is LLM. So a large language model is a type of neural network based model designed to process and generate human language. So LLMs are built using deep learning techniques that are typically trained on enormous data sets containing billions or even trillions of words. They consist of billions of parameters which are the weights of connections in the neural networks which makes them extremely powerful at understanding and generating language. So the core idea of LLM is to use statistical patterns in text to predict the likelihood of a word or phrase given its context enabling them to generate contextually relevant and humanlike responses. So for example, models like GPT3 by OpenAI or BERT by Google can be fine-tuned to perform specific tasks like answering questions, translating languages or summarizing text. So let's look at the architecture behind the LLMs which are transformers. So the architecture behind LLMs are the transformers. We have already uh you know discussed about transformers in detail in the previous chapter. So first is the self attention mechanism. Uh so the self attention mechanism as already discussed is central to transformers and it allows the model to weigh the importance of each word in a sentence relative to every other. So for example in the sentence the cat sat on the mat the word cat is most likely related to sat than to mat right? So the self attention mechanism helps the model capture these relationships allowing it to understand the context better. So the model also calculates attention scores by comparing each word with every other word in the input sequence. Next we have multihead attention. This also we had discussed before. So transformers use multi-head attention where the attention mechanism runs in parallel across multiple sets of attention heads. So each head learns to focus on different aspects of the input sequence which helps the model capture diverse relationships and patterns within the data. And then we have the positional encoding. So since transformers do not process data sequentially, they use positional encoding to ensure that the model knows the position of each word in the sequence. So this encoding is added to the input embeddings allowing the model to understand the order of words and how context is built throughout a sentence or a paragraph. And the next step is training large language models where we have pre-training where the model is first pre-trained on vast text data which is a process known as unsupervised learning. So in this phase the model learns to predict the next word in a sentence. So during pre-training the model develops a deep understanding of how language works by looking at the relationships between words, phrases and sentences. And then we have fine-tuning. So after pre-training the model is fine-tuned using supervised learning on more specific data sets for specialized task such as question answering, text classification or summarization. So in this phase, the model learns how to apply it language understanding to specific use cases. Next, let's see the key features of LMS. So there are several features which contribute to the remarkable performance of large language model. So let's see one by one. The first one is the scale of parameters. So LLMs are called large because they have an enormous number of parameters, right? The learnable weights within the model. So for instance, GPT3 has 175 billion parameters while some newer models like GPT4 or PAL which stands for pathways language model have trillions of parameters. So the larger the number of parameters, the more data the model can process and the more complex language patterns it can learn. So the performance of LLM improves significantly as the number of parameters increases and this is known as scaling. For example, parameter scaling. So larger models can better capture linguistic nuances, understand longer texts and generate more humanlike text. And then the next parameter, the next we have zeros short, few short and many short learning. So LLMs are capable of performing tasks without needing extensive task specific training. These models are the first one is zeroot learning. This means basically performs tasks without any examples. So for example, a well-trained LLM can answer a question it has never been explicitly trained to answer. Then we have few short learning which performs tasks with just a few examples to guide the model. So for instance, if given a few examples of how to summarize articles, the model can generalize and apply this to new articles and then we have many short learning which performs tasks with many examples which is the typical case for fine-tuning on a specific data set. So this flexibility allows LLM to handle a wide range of applications without needing extensive retraining for each task. And then we have contextual understanding. So LLMs have a deep understanding of context. They can maintain coherent long-term dependencies making it possible to generate text that is not only grammatically correct but also contextually appropriate. So for example, GPT3 can generate long paragraphs of text that make sense logically and follow the prompt closely showing a remarkable understanding of the relationships between concepts, facts, and reasoning. So now let's see some of the applications of LLM. It can be used in text generation. So LMS can generate highquality humanlike text for variety of purposes such as article writing, creative writing and advertising. It can be used in machine translation. So LLMs like GPD3 and BERT are used in automatic translation systems providing accurate translations between languages without requiring a direct mapping. Then it is also used in summarization. LLM are used to generate summaries of long documents, articles or books. Then it's also used in question answering. So by understanding and processing the context of a question LLM can provide precise relevant answers to a wide range of queries. So this application is mainly useful for virtual assistants like Siri, Alexa and customer service boards. Thus it's used in sentiment analysis. So LMS can be trained to classify and interpret the emotional tone of a piece of text such as detecting whether a tweet or a review is positive, negative or neutral. So this is widely used in social media analysis and market research. And it can also be used as told before in chat bots and conversational agents. So LLMs are widely used in chat bots, virtual assistant and customer service applications. So now let's see some of the challenges and limitations of LMS. So while LLMs are incredibly powerful, of course they are not without their challenges and limitations. So the first one being bias and fairness. Now LLMs can inherit biases present in the training data which can result in unfair or unethical output such as gender or racial bias and text inclusion. So this is a major concern in AI ethics. Then comes a computational cost. So training LLM requires vast computational resources which can be expensive and environmentally costly. And then comes a problem of interpretability. So LLMs are often considered blackbox models meaning it can be difficult to understand exactly how they make decisions. So this lack of transparency can be problematic in high stakes applications like in healthcare or law. And then we have the data privacy concerns. So LLMs as you know are trained on publicly available data and it may inevitently memorize and reveal sensitive or private information raising concerns about data privacy and security. So next let's see when does AI generated content works well and when it doesn't work well. So first let's see when does AI generated content works well. So the first one is in repetitive and structured tasks. So AI excels in generating content for tasks that follow a predictable structured pattern. So this includes tasks like product descriptions. So AI can generate bulk product description particularly when the format is consistent. For example, tools like copy AI or Jasper can automate the creation of thousands of product descriptions in no time and then can be used in reports and datadriven content. So AI can generate datadriven reports based on structured data for example financial results, sports scores or weather reports and it can also be used in email and marketing campaigns. So AI can generate personalized marketing emails or social media posts quickly and efficiently. Next, it can be used in content personalization. So AI can analyze vast amounts of data to create highly personalized content and this is beneficial in areas like email marketing. So AI can write personalized subject lines, content and calls to action based on a customer's browsing history, purchase behavior and preferences. So for example, AIdriven tools like Padu are already helping brands personalize their marketing materials at scale at large scale. And then we have recommendation systems. So as we all know in platforms like Netflix, Spotify or Amazon, AI generated content recommendations. It can be like movies, songs or podcasts are highly effective at suggesting items. users are likely to enjoy based on their past behavior. Then we have areas of creative and experimental work which includes art and design. So AI models like Dari 2 and Mjourney are incredibly effective in creating visual art, illustrations or design concepts from text prompts. So designers use AI to quickly generate design options and explore creative possibilities. And then comes the storytelling. So AI generated stories or scripts can help authors, screenwriters or content creators generate ideas or overcome writer's block. So tools like charg can draft stories or plot lines based on simple inputs helping authors develop initial drafts or experiment with different narrative structures. And then it can be used in music composition also. Next, it can be used in large scale content production. So for large scale content production where speed is critical, AI can read in vast quantities of content rapidly. So for example, content for SEO. AI can generate blog post, articles or web copy optimized for SEO. Helping businesses keep their websites up to date with fresh keywordri content. And then in content creation or content curation. So AI can help with curating content from various sources such as news aggregation by pulling relevant articles and data. And then it can also be used in language translation. So AI models particularly those based on transformers like Google translate and deep learn are highly effective at translating text between languages. So these systems can handle vast amounts of text and provide realtime translations. So now that we have seen when AI generated content works well. Next let's see when it doesn't work well. So the first one is in creative nuance and context. So while AI can generate creative content, it often struggles with deep emotional nuance. So AI often lacks a true understanding of human emotions and struggles to generate content that truly resonates on a deep emotional level. So for instance, a piece of content that's meant to evoke compassion, uh, humor or grief might fall flat because the AI doesn't genuinely feel those emotions. And then there is cultural sensitivity issue. So AI models trained on large data sets can inadvertently produce content that is culturally insensitive or inappropriate. Next is originality and intellectual property concerns. First one is lack of true originality. So AI is capable of producing content that appears original but is fundamentally based on patterns it has learned. Right? So this means AI generated content can sometimes lack true originality which you might have faced while generating content and can unintentionally replicate the existing ideas or phrases and then there is a risk of plagarism. So AI tools trained on large data sets may generate content that's too similar to existing work leading to plagarism concerns. The next there is complex tasks requiring expertise where highly technical writing comes. So while AI can generate content on a variety of topics, it struggles with complex specialized subjects like advanced scientific research, intricate legal analysis or deep medical knowledge. So for instance, in areas like legal documentation, the precision and the attention to detail required often go beyond AI's current capabilities. And then there is subjective judgment. So AI lacks the ability to make subjective or qualitative judgments that are often needed in areas like art, criticism, literature analysis or evaluating ethical dilemmas. And then there is bias and ethical concerns which we have already discussed in the limitations. And then there is quality control and inconsistent output. So although AI has made significant strides in content generation, it often still struggles with maintaining quality, consistency and governance. And then there is legal and comprehens like healthcare or finance AI generated content needs to follow strict guidelines. So for instance an AI generated financial report or medical advice might inadvertently violate legal regulations or industry standards. And then there is also an issue of copyright. So AI generated content especially art and writing may raise questions regarding ownership. Who owns the rights to content generated by an AI? Is it the creator who prompted the AI or the company that developed the model? So these legal gray areas can create complications in industries where intellectual property rights are crucial. So now in the next chapter we'll see how generative AI is used in businesses. How does companies use generative AI? So AI is increasingly being adopted by businesses across various industries to improve productivity, save time and streamline operations. So here's a detailed breakdown of how businesses are leveraging AI to achieve these goals. So the first one is AI powered chat bots for customer support. So one of the most common uses of AI in business is through AI powered chat bots which are transforming customer service by providing instant roundthe-clock support. So here's how it works. First is instant customer interaction. So AI powered chatbots are available 24 bar 7 and can handle multiple customer queries simultaneously. So they can answer frequently asked questions like FAQs uh provide information about products or services and process simple transactions and resolve common issues. So for example live person a company offering AI chat bots pass brands like Vafoon and HSBC. So these bots can handle inquiries about account balances service status and more significantly reducing the wait times for customers. Then there is cost efficiency. So with AI chatbots handling routine inquiries, businesses reduce the need for a large human customer support team. This leads to cost savings on staffing and training while maintaining a high level of customer satisfaction. So for example, Sephora, the cosmetics retailer, uses an AI powered chatbot named Sephora virtual artist to assist customers in finding makeup products based on their preferences, giving personalized recommendations and beauty advice, and then it leads to improved customer satisfaction. So AI chatbots provide quick and consistent responses reducing customer frustration. They also have the ability to analyze customer sentiment and escalate the complex issues to human agents when necessary. So for example, Mitsubishi Electric uses AI chatbots to quickly address technical inquiries from their customers, streamlining the support process and ensuring faster response time. So next, it