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Generative AI: A Comprehensive Use case Guide

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Generative AI or Gen AI or Generative Artificial Intelligence is a rapidly-growing field of machine learning (ML) that uses generative models to generate new data.

As the world becomes increasingly data-driven, generative AI has the potential to revolutionize a wide range of industries and datasets.

In this comprehensive guide, we’ll outline the different types of generative AI, describe how they work, and discuss some of the applications and future of this fascinating field.

So whether you’re a beginner looking to learn more about generative AI or an experienced machine learning practitioner interested in expanding your arsenal, this guide is for you!

Photo by DeepMind on Unsplash generative AI
Photo by DeepMind on Unsplash

What is Generative AI or Gen AI?

Generative AI is a branch of machine learning that has the potential to inspire a variety of industries. It allows machines to create new objects, patterns, and solutions.

This technology can transform healthcare, manufacturing, and many other industries.

However, before implementing generative AI into your business, it’s vital to understand what it is and how it works.

Additionally, implementing generative AI can be a complicated process, but with the help of the right tools and resources, one can do it quickly and effectively!

How does generative AI work?

Generative AI is a category of artificial intelligence that involves creating models capable of generating new data, such as images, text, audio, or other types of content, that is similar to what it has been trained on. Generative AI aims to mimic human creativity and generate novel, realistic outputs.

Gen AI is a branch of AI that involves artificial neural networks to generate new data. This data will then help to improve the neural network performance or to create new models or strategies for solving problems. There are several different ways in which generative AI is handy. Some applications involve using neural networks to generate new images, sounds, and other types of information.

Others involve using them to create new types of algorithms or models that can help solve problems more effectively. And still, others are using them to create new forms of art or entertainment.

The potential applications of generative AI are virtually limitless, and its impact on the creative industries is just beginning.

The Concept and Applications of Generative AI

Generative AI is a powerful technology with a wide range of applications. 

Stakeholders can leverage AI to create innovative solutions across different industries. Machine learning algorithms drive the workflows of generative AI, enabling automation in content creation.

The market for generative AI may reach $109 billion by 2030, highlighting its disruptive potential.

In today’s world, where data is abundant from various sources like social media and the internet, generative AI provides a fresh perspective and new ideas.

With its ability to generate high-quality and personalized content, generative AI has become a game-changer in fields like building design and creative industries.

It relies on natural language processing, neural networks, and deep learning techniques to produce high-performance results.

However, we must address ethical concerns, including data privacy and collection, to harness the full potential of generative AI.

How to architecturally approach Generative AI Development?

When architecturally approaching Generative AI development, consider the below Gen AI approach model, which is by far the best Architectural Approach for Generative AI.

1 – What will be the foundation model for your Generative AI application?

When architecturally approaching Generative AI development, the choice of foundation model is the base.

Depending on your specific use case and data, you can consider models like Variational Autoencoders (VAEs) or Generative Adversarial Networks (GANs).

What is VAE and GAN?

VAEs are great for encoding and decoding complex data, while GANs can generate new data based on existing samples. Careful selection and training of the correct model are essential for a successful Generative AI application.

2 – Define where to deploy and how to access the Gen AI App.

Do some shopping! Find out if your application needs cloud-based platforms like AWS, Azure, or Google Cloud.

Additionally, look for on-premise deployment using servers and hardware that meet the app’s requirements.

With this sorted, next think of access to the Gen AI app through secure login credentials and integration with other software tools that allow for a seamless workflow.

3 – How will the model adapt to internal training data? (Helpful, Harmless, and Honest)

When considering how the model will adapt to internal training data, prioritize helpfulness, harmlessness, and honesty.

  • Helpfulness: Ensure that the Generative AI model provides valuable and accurate insights. You can do this by continually iterating and fine-tuning the model based on user feedback and performance evaluation.
  • Harmlessness: Mitigate any potential risks or biases associated with the Generative AI model. Regularly monitor and test the model for fairness, transparency, and accountability to avoid unintended consequences or negative impacts.
  • Honesty: Transparency in the training process ensures trust and integrity in the Generative AI model. Communicate the limitations, assumptions, and model biases to users so they understand its capabilities and potential shortcomings.

Explore more on how AI can be helpful, harmless, and honest with our dedicated article here.

4 – What are the mandatory elements to bake in while building a Gen AI app?

Let’s keep it simple to 3Rs and 3Is,


  • Resilience: Think of it as the security of the app. How will you secure the enterprise, data, and operations from potential threats or breaches?
  • Reliable: The Gen AI app must consistently deliver accurate and reliable insights.
  • Responsible: Transparency and accountability are essential in building a Gen AI app. Build a process around the model to address the potential unintended consequences or impacts.


  • Integration: Ensure the Gen AI app seamlessly integrates with existing systems and workflows. Consider how the app can efficiently process and analyze data from various sources.
  • Interoperability: The Gen AI app should be able to communicate and work with other applications or platforms, allowing for easy data exchange and collaboration.
  • Ingenuity: Think of three words, Inventive, Creative, and Resourceful. Can your Generative AI app satisfy these factors from the end user’s perspective?

5 – Is this Gen AI app Sustainable and Scalable as you grow?

As you consider the sustainability and scalability of your Gen AI app, there are several factors to keep in mind.

Firstly, sustainability refers to the ability of your app to continue functioning efficiently and effectively over time. To ensure this, design your app with modularity and flexibility in mind. It allows for easy updates and improvements as technology evolves.

Additionally, consider the environmental impact of your app. Are there ways to optimize energy usage or reduce carbon footprint?

Scalability is the ability of your Gen AI app to handle increasing demands and adapt to growing user needs. To ensure scalability, design your app with a modular and distributed architecture.

Furthermore, consider the potential for future expansion and integration with other systems. Can your Gen AI app seamlessly integrate with new data sources or work alongside other applications?

6 – Can this approach be on auto-pilot with minimal operations structure?

The goal should be to automate processes and minimize manual intervention for a Gen AI app operational structure.

You can achieve an auto-pilot mode with minimal operational requirements by using advanced automation capabilities.

Consider implementing intelligent algorithms and machine learning models that handle routine tasks and decision-making without human intervention. 

It will not only streamline operations but also reduce errors and enhance efficiency.

Free courses for you to explore from Google

Are you planning to use Google’s Generative AI Studio?

Generative AI Studio is a valuable tool available on the Google Cloud console that allows for quick prototyping and experimentation with generative AI models.

With this tool, you can test out sample prompts, create and save your own prompts, and customize foundation models to suit the specific requirements of your application.

Generative AI Studio offers a range of functionalities, including the ability to test models using prompt samples, design personalized prompts, fine-tune foundation models, and even convert between speech and text.

Here is the link to access Google’s Generative AI Studio on Vertex AI. Go explore.

Applications of generative AI

There are a variety of applications for generative AI, including:

Artificial General Intelligence

Artificial intelligence (AI) research impacts by creating “artificial general intelligence” or AGIs that can effectively solve various tasks in the cognitive domain.

However, much work remains to be done before AGIs can be considered truly intelligent. Many researchers believe that generating art is vital in AI development because it demonstrates that an AGI has acquired some form of creativity.

Text Generation

Text generation is another application area where generative AI has seen significant progress. This field is particularly important for natural language processing (NLP), which is concerned with understanding human language and extracting meaning from it. These text models can automatically produce grammatically correct texts that are semantically coherent, realistic, and plausible.

Generative Adversarial Networks (GAN)

Generative adversarial networks (GANs) are another generatively trained machine learning algorithm that is extensively used in computer graphics and video editing applications. 

GAN architecture consists of two neural networks: a generator network that produces outputs that look like the input images and an adversary network designed to identify errors in the output images. By training the generator network using labeled examples of desirable

Generative Pre-trained Transformer (GPT)

Generative Pre-trained Transformer (GPT) models are a new type of deep learning model inspired by early stop-and-copy neural network architectures. 

GPT models use a generative adversarial training algorithm to learn a transformer between two input and one output layer. It generates data in images and text representing transformations between different domains or modalities, such as audio data converted into text or facial data translated into an image.


-DALL.E 2 is a platform that makes it easy to create, test, and deploy generative models. It offers powerful features for machine learning training, including graphical programming environments and ensemble methods for boosting performance on large datasets.

The potential applications of generative AI continue to grow as researchers unlock new ways to apply machine learning algorithms to solve complex problems.

The different types of generative AI

There are several different types of generative AI.

Some examples include deep learning, reinforcement learning, artificial neural networks (ANNs), and genetic algorithms.

Each type of generative AI has its strengths and weaknesses.

AI Image Generation

Nowadays, AI image generation plays a significant role in many industries. Apart from providing accurate images for various purposes (such as security and authentication), it also helps speed the process of image processing.

One such type of AI is extensively using deep learning techniques is generative adversarial networks (GANs).

GANs are neural network model that uses two competing algorithms – generator and discriminator – to create realistic images.

The key benefit of using this technology is its ability to generate images that are indistinguishable from those produced by humans.


Text-to-image (T2I) technology is a powerful tool that creates realistic images from text. However, it has certain limitations that need attention when using this technology.

Recursive neural networks are the most advanced type of generative AI and have proven to produce high-quality images.

However, they require a lot of training data to function effectively. Adversarial neural networks are the perfect type of AI for T2I purposes, and they can reproduce any image or object given enough training data.


Text-to-speech is a form of generative AI to generate humanlike voices for computer applications.

It has become the most commonly used generative AI because of its easy implementation into business applications, offers many benefits, and helps automate tedious processes.

Machine learning automatically learns from data and improves performance over time.

This technology can also help improve communication and customer experience by automating tasks such as emailing your customers about product updates or providing live support with a friendly voice.

The new Xrai glasses are promising in the speech-to-text area, you can explore their products here.

Check out the below platforms:


Audio AI

Audio AI is a rapidly growing field with potential for new businesses.

Generation of audio, or the ability to create new soundscapes, stands out as the most promising application of audio AI.

With the help of machine learning and artificial intelligence, AG can produce sounds genuinely unique and delightful – which is tough to achieve using traditional methods.

Additionally, speech recognition and natural language processing are expanding rapidly due to their wide range of applications in customer service systems and various other fields.

While all three areas hold tremendous potential for growth soon, AG is likely to be the biggest beneficiary due to its unparalleled potential to enhance user experience.



Video AI

Video AI is a technology that allows artificial intelligence to create and edit videos. It can learn and improve over time, making it a powerful tool for content marketers.

Here are some key benefits: – Video AI can improve video quality by removing noise and improving audio clarity.

It makes videos more user-friendly for viewers, who will no longer have to strain their ears trying to understand the message. It also creates short, consistent videos that are easier on the eye.

Videos with good visual aesthetics tend not only to be more engaging but also persuasive due to their natural appeal. By automating the process of video creation, video AI reduces the workload faced by content creators – freeing up valuable time that could be better used elsewhere in a business operation or marketing campaign.


Image and Audio Resolution Enhancement by AI

AI can improve image resolution and audio quality by using machine learning algorithms. It makes images and audio files look sharper, with a greater level of detail.

This technology is particularly useful for media professionals who need to produce high-quality content but only have limited resources or bandwidth restrictions.

It also has a range of other applications, such as video game design, medical imaging, and security scanning.

Synthetic AI data generation

When artificial data is in consideration to train the AI algorithm, it is known as synthetic data generation. This data helps to improve the performance of ai systems.

It can be generated using several methods, such as copying real-world data sets and adjusting them until they match the desired characteristics for training a machine learning algorithm.

The synthetic data can then train machine learning models that apply to new datasets to improve their performance.

What are the challenges of Generative AI?

If Generative AI becomes a mainstream technology, some challenges need attention. Chief among these is the difficulty of learning to generate meaningful and intelligible content.

Another challenge is the need for robust and reliable data sources. Without good data, building models is tough to generate credible predictions. Additionally, there is still much work to be done in developing algorithms that generate content in different formats (text, images, etc.).

Meanwhile, some people argue that Generative AI may ultimately lead to feelings of loneliness and angst in humans as machines become more intelligent than we are. They see it as a potential threat to our ability to create and understand art. However, this opinion has yet to gain much traction among the general public.

The scary side of AI.

There are a few potential negative consequences of AI that we must consider. For example, it could lead to robots taking over many jobs and causing large-scale economic disruptions.

AI could also harm our social institutions and culture. It could create massive divides between rich and poor or between people who have access to technology and those who don’t. It could also lead to the emergence of new forms of discrimination and prejudice.

It’s important to realize that these are only potential dangers that still require further research and exploration. However, it’s also important to remember that AI has the potential to do great things for humankind – like making our lives easier and more fulfilling. So long as we’re aware of the risks and handle them responsibly, everything will ultimately turn out okay.

Generative AI Models

We will discuss the Discriminative and the Generative models.

Discriminative Model

A discriminative model is a machine learning algorithm to distinguish between different objects in a scene. This model typically uses a trained neural network, which can identify specific features of an object.

This algorithm is for tasks such as image recognition, text recognition, etc. AI models are becoming wildly popular because of their accuracy and efficiency.

Generative Model

Generative AI models are a special type of artificial intelligence that can generate new data. They are for tasks such as image recognition, text processing, and natural language understanding.

They can be trained using a large amount of data and then applied in different scenarios. There are several types of generative AI models- Convolutional Neural Networks (CNNs), Recursive Neural Networks (RNNs), and Self-Organizing Maps (SOMs).

Use cases

FieldUse Case
Marketing and Sales1. Personalized Product Recommendations
2. Customer Segmentation and Targeted Advertising
3. Generating Product Descriptions and Ad Copy
4. Content Creation for Social Media Campaigns
Operations1. Predictive Maintenance
2. Inventory Optimization
3. Workforce Scheduling and Planning
4. Supply Chain Optimization
IT/Engineering1. Code Generation and Automation
2. Testing and Quality Assurance
3. Network Configuration and Optimization
4. Fraud Detection and Security
Risk/Legal1. Contract Generation and Review 
2. Compliance Monitoring and Reporting 
3. Predictive Risk Management
4. Fraud Detection and Prevention
HR1. Resume Screening and Shortlisting 
2. Interview Question Generation
3. Employee Engagement and Retention Analysis
4. Talent Acquisition and Recruitment
Employee Optimization1. Performance and Skill Assessment
2. Employee Training and Development Recommendations
3. Workload and Task Allocation Optimization
4. Career Progression and Succession Planning
Healthcare1. Diagnosis and Treatment Recommendations
2. Medical Image Analysis and Interpretation
3. Clinical Trial Design and Optimization
4. Electronic Health Record Generation and Management
Finance1. Credit Scoring and Loan Underwriting
2. Portfolio Optimization and Risk Management
3. Financial Planning and Budgeting
4. Fraud Detection and Prevention in Financial Transactions
Agriculture1. Crop Yield Prediction and Optimization
2. Livestock Health Monitoring and Management
3. Irrigation and Water Resource Management
4. Precision Agriculture and Smart Farming
Energy1. Energy Demand Forecasting and Management
2. Renewable Energy Integration and Optimization
3. Power Generation and Transmission Planning
4. Energy Efficiency and Conservation
Retail1. Customer Behavior and Trend Analysis
2. Inventory Management and Optimization
3. Personalized Marketing and Advertising
4. Store Layout and Design Optimization
Transportation1. Route Optimization and Scheduling
2. Fleet Management and Maintenance
3. Traffic Prediciton and Management
4. Autonomous Vehicle Navigation and Control
Environment1. Weather Forecasting and Climate Modeling
2. Natural Resource Management and Conservation
3. Pollution Control and Reduction
4. Disaster Prediction and Response Planning
Manufacturing1. Quality Control and Inspection
2. Supply Chain Optimization and Management
3. Predictive Maintenance and Equipment Optimization
4. Process Optimization and Automation
Education1. Personalized Learning and Recommendation Systems
2. Test Generation and Assessment
3. Content Creation and Curriculum Development
4. Student Performance and Progress Monitoring
Media1. Content Generation and Recommendation Systems
2. Sentiment Analysis and Opinion Mining
3. Video and Audio Editing and Enhancement
4. Social Media Monitoring and Analysis.
Generative AI Use Cases

To top this up McKinsey just released their State of AI 2023 survey results, take a look for yourself. Also McKinsey reports that in 2021-22, there has been a 44% increase in job postings for Gen AI with a $5B equity investment.

AI tools for the new generation

Generative AI companies

Insilico Medicine

Mostly AI




Synthesis AI

Eva Engines

Revery AI Inc.



Veesual AI



Design Interactive Inc.




UAB Biomatter Designs


Genie AI

Stability AI

Open AI

Future of generative AI

The future of generative AI is still largely unproven, but its potential is enormous. If it can successfully create new and original items or concepts, it could revolutionize how we create culture and products. It could also lead to the development of completely new industries and occupations.

Many challenges need attention before generative AI can truly take off, however. One major issue is its limitations in understanding how humans think and operate. As a result, it’s often tough for generative AI to generate items or concepts that are truly unique and novel. 

Nevertheless, there’s no doubt that generative AI will play a vital role in the future of the creative industries – so keep your eyes open and follow this blog for more updates.

Soon we will publish more about unsupervised learning, supervised learning, data augmentation, training sets, GPT-3, and other use cases.

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Frequently Asked Questions

What are the essential components of generative AI?

With generative AI, the three main components are dataalgorithms, and models.

Data is the raw input that goes into the algorithms and models that helps in generating new outputs.

Algorithms process the data and generate new outputs based on rules.

Models are used to make predictions or forecasts about future scenarios based on previous inputs of data and algorithms.

What is Generative Neural Network?

Generative Neural Networks (GANs) are a type of AI that allows machines to generate new content. It is different from traditional AI, which relies on rules or programmed instructions to accomplish a task. GANs, in contrast, are equipped with the ability to learn and create things on their own – without any pre-determined inputs.

GANs have been used for many tasks in the past, including art, music, and text generation. They can generate anything you want – from logos to complex designs.

One of the biggest benefits of GANs is that they’re able to simulate the workings of the human brain. That means that they can mimic the way humans think and process information. It makes them perfect for tasks like generating novel ideas or designing unique and creative products.

What are Generative algorithms?

Generative algorithms are a type of AI to generate new data. It is by combining different pieces of information to create something new.

There are a few different generative algorithms, but probabilistic and chaotic algorithms are common. Probabilistic algorithms use probability to decide which piece of data should be generated next. Chaos algorithms use chaos theory to create random pieces of data.

Both of these types of algorithms have their benefits and drawbacks. Probabilistic algorithms tend to be faster and more accurate, while chaos algorithms can produce interesting and unpredictable results. However, they’re also less reliable and can often produce errors.

Overall, Generative AI is a powerful tool for creative industries to generate new ideas and data. There is potential to change how we think about creativity and how it works.


In this blog, we discussed the different types of generative AI, the importance of generative AI, how to build an Gen AI app, and the future of generative AI. You now have a comprehensive understanding of the topic and will be able to make more informed decisions about the future of generative AI.

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