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June 14, 2024

Generative AI Implementation: A step-by-step guide


Artur Haponik

CEO & Co-Founder

Reading time:

14 minutes

According to a recent study by McKinsey, generative AI could add about $2.6-4.4 trillion to the global economy annually across 63 use cases. This would easily increase the global impact of AI by 15-40%. [1]

However, to harness this technology’s transformative power, organizations must have a well-defined plan for Gen AI implementation.

This post will provide you with an in-depth review of what generative AI is and a detailed step-by-step guide on how to implement generative AI into your business.


Understanding Generative AI

Generative AI is a type of AI technology capable of creating new and realistic content in response to prompts. Basically, this technology uses artificial intelligence and machine learning algorithms to create a variety of content, such as:

  • text
  • audio,
  • images
  • videos
  • software code
  • product designs
  • and even synthetic data.

For optimal functionality, generative AI relies on neural networks and advanced algorithms to identify the underlying patterns in the input data and generate relevant content that follows similar patterns. This process involves learning about the distribution of the training data and using it to generate new and original novel content that follows the learned patterns.

Notably, the most powerful generative AI algorithms are usually built on top-foundation AI models, which are trained using huge amounts of unlabeled data through self-supervised training. Complex math and immense computing power are also needed to create these pre-trained AI models.

Some of the key concepts in generative AI include:

key concepts of generative ai

  • Variational Autoencoders (VAEs)
    These are some of the most fundamental models used in generative AI. The generative AI models usually combine the power of both encoders and decoders to capture the true essence of input data by mapping it into a lower-dimensional latent space. From there, the decoder then generates new samples that resemble the original input data. They are used in various applications, including text synthesis and image generation. [2]
  • Generative Adversarial Networks (GANs)
    GANs consist of a generator and a discriminator. They are deeply involved in creating new data outputs that resemble your training data. The generator mainly focuses on generating realistic samples, while the discriminator distinguishes between real (from the domain) and fake (generated) samples. With the help of this competitive interplay, GANs learn to generate more realistic and high-quality outputs. Over the years, GANs have been used in generating videos and images and even simulating human voices.
  • Transformers
    These AI models have helped revolutionize natural language processing and image tasks. Transformers mainly use self-attention mechanisms to capture long-range dependencies and relationships in sequential data. This makes them capable of generating coherent and contextually relevant texts and images.

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How to implement Generative AI: A step-by-step guide

The emergence of popular generative AI models like OpenAI’s GPT, Google’s Bard, and Stability AI’s Stable Diffusion has made it clear that generative AI is here to stay. You can make this technology work in your favor by setting a solid plan for its implementation.

Here are steps to follow to implement generative AI:

How to implement Generative AI into your business? Infographic

Understand the problem and identify the potential use cases

The first step in applying generative AI is to understand the problem you’re trying to solve with this technology. Once you’ve identified the problem, you should also identify areas where generative AI will drive the most value in your organization.

That said, you need to map out a list of use cases with the highest potential based on your organization’s objectives, strengths, and weaknesses. Once you’ve identified the potential use cases of generative AI in your organization, proceed to prioritize them based on value potential, data availability, time to value realization, implementation complexity, and available computation resources. It’s highly recommended to focus on use cases where generative AI can solve pressing problems quickly.

For example, if your organization has been dealing with high energy bills, it may be best to use generative AI models to analyze energy consumption trends and optimize energy usage. Additionally, if you’ve been struggling with long wait times in your customer support department, consider prioritizing generative AI-powered chatbots and virtual assistants. These will help you respond to many customer queries simultaneously, thus taking the pressure off your customer service agents.

Prototyping stage

Like with any software development phase, generative AI implementation requires prototyping. In the prototyping phase, you will build a preliminary version of the generative AI model to test its feasibility and functionality.

This process may involve experimenting with different algorithms, AI models, datasets, and architectures until you find the optimal solution. Notably, prototyping uses smaller datasets and simpler AI models to help provide quick feedback and allow fast iteration of the design. The best thing about prototyping is that it helps developers identify any loopholes in the design and fix them in good time.

There are several steps involved in the prototyping phase, including:

Data collection

Data collection is a vital step during generative AI implementation in the prototyping phase. [3] You need to gather the data you will use to train and test your generative AI model. This way, the model will be able to learn and generate high-quality outputs based on the patterns and trends it has identified in the data.

When collecting data, you need to:

  • Identify the data sources you will use to train and test your generative AI model. This may include both structured and unstructured data from various sources such as social media platforms, web pages, articles, and reviews.
  • Ensure the data collected is high-quality and relevant. The data should also be diverse enough to convey the problem areas the generative AI model intends to solve.
  • Carry out data labeling to help provide context to the training data so that your generative AI model can learn from it. This will help reduce the labeling costs and increase the overall accuracy of the generative AI model. Some techniques you can use for data labeling include crowdsourcing, active learning, or semi-supervised learning. [4]
  • Preprocess the data before feeding it into the generative AI model. This can be done using data augmentation and tokenization techniques to help generate a suitable format of data understandable to the generative AI model.
  • Split the data into training, validation, and testing sets. The training dataset is used to train the model, while the validation dataset is used to fine-tune the hyperparameters and evaluate the model’s performance. Lastly, the testing set is used to test the final model’s performance. [5]
  • Ensure you store the data in a format easily accessible for the prototyping and development phases. You can choose to store the data in a cloud-based storage, data warehouse, or a distributed file system.

Data preprocessing

The next step in the prototyping phase is to process the data to ensure quality, relevance, and consistency. Some of the tasks involved in data preprocessing include:

  • Data cleaning: Remove noisy, incorrectly formatted, duplicate, corrupted, irrelevant, or incomplete data.
  • Data normalization: It ensures its consistency across the entire dataset. Some techniques involved in data normalization include scaling, standardization, and centering.
  • Data tokenization: Convert the data into a set of tokens and symbols that the generative AI model can understand.
  • Feature extraction: Divide and reduce the large collection of raw data into more manageable groups for easier processing. [6]
  • Data augmentation: Enhance the size and diversity of the training data by generating new data points from the original data.
  • Labeling: Assign labels to the data to indicate which category or class it belongs to.
  • Validation: Validate the preprocessed data.

Select the appropriate generative AI algorithms

Choosing the right generative AI algorithms is crucial for efficient generative AI implementation. This is because the algorithms chosen will determine the quality and accuracy of the output generated. Notably, different generative AI algorithms have different strengths and weaknesses.

When choosing the generative AI algorithms to use, there are various factors you should consider to achieve the desired results. These factors include:

  • The problem area: Ensure you understand the problem area the generative AI intends to solve to help narrow down the list of algorithms.
  • The framework: Select the right deep learning framework suitable for the task.
  • Evaluate the available generative AI algorithms: Ensure you evaluate the performance of different generative AI algorithms on the preprocessed data.
  • Consider the computational requirements: Before choosing a generative AI algorithm, ensure you have the necessary computational resources ready.

Set up a proper development environment

Setting up a proper development environment with the necessary tools and resources helps developers effectively create, test, and deploy AI models. In addition to promoting collaboration among team members, such an environment also ensures that the implementation of generative AI can be scaled and maintained over time.

The development phase

In the development phase, you get to build the full-scale generative AI model using the selected generative AI algorithms and datasets. The main goal of this phase is to enhance the efficiency, performance, and robustness of the generative AI implementation for its long-term success.

To ensure the successful completion of the development phase:

  1. Choose the right data storage
  2. Select the right data processing frameworks
  3. Design the code for parallelization to enable the use of multiple GPUs and CPUs.
  4. Use cloud computing to handle large volumes of data and users.
  5. Use containerization to deploy data and code across various environments.
  6. Implement caching to minimize the amount of data access requests.
  7. Optimize the code for performance to improve efficiency and reduce the processing time.


In this phase, proceed to deploy the generative AI model to the respective productive environment and make it available to end-users. To succeed in the deployment of the generative AI model, you must configure the model to ensure its security, reliability, and efficiency. This will ensure a greater user experience and satisfaction.

Empower your business with the expertise of a Generative AI development company in implementing Gen AI.  

Why Generative AI is essential for your business?

Here are some reasons why you should implement generative AI into your business processes:

Enhanced creativity

One of the best things about generative AI implementation is that it enables users to generate original and creative content on a large scale. With the help of neural networks and advanced algorithms, these AI models can easily generate outputs beyond human capabilities.

Artists, content creators, and graphic designers can use generative AI capabilities to generate unique art pieces, innovative designs, and compelling video content thus reducing the need for extensive creative work. In other words, generative AI helps streamline the creative process and opens new possibilities for innovation.

It might be interesting for you: LLM implementation strategy: Preparation guide for using LLMs


Generative AI can analyze huge amounts of customer data and generate customized product recommendations, content, and experiences based on individual preferences. Additionally, generative AI bots can adjust their languages to fit customers’ preferences or provide instant translations to customer support agents.

This level of hyper-personalized customer service enhances customer engagement and satisfaction, leading to improved customer loyalty and retention.

Better decision making

At the core of generative AI are multilayer neural networks capable of analyzing huge amounts of data and generating insights that lead to informed decision-making. Additionally, generative AI models can identify trends, patterns, and correlations that may not be so obvious to human analysts. This can help organizations develop effective strategies and policies that result in greater operational efficiency and profitability.

Improved customer service

Maintaining fast response times can be quite difficult, especially if your company has many customers. If you don’t have as many team members, customers may be forced to wait too long to speak to a customer service representative. Fortunately, this is not a major problem for organizations that have adopted generative AI.

Nowadays, generative AI-powered chatbots are proficient in understanding and responding to common customer inquiries like providing product information, offering troubleshooting guidance, and assisting with order tracking. These chatbots are also capable of generating instant responses to customer inquiries, which means that customers no longer have to wait in lengthy phone queues.

Most importantly, generative AI chatbots do not need any breaks or vacations. Therefore, by integrating these chatbots into their customer support systems, organizations can ensure they offer around-the-clock customer support and address any customer concerns without delay. In addition to improving customer service, this 24/7 customer support reduces the need for human intervention and significant cost savings for organizations.

Improved efficiency

Generative AI implementation can increase efficiency in workplaces by automating repetitive tasks and workflows. This reduces the risk of human errors and frees up time for more strategic and innovative thinking.

For example, financial institutions can use generative AI to come up with personalized investment reports or handle data entry. Marketers can also use generative AI to generate an outstanding marketing plan in a matter of seconds, a task that would normally take hours to complete.


The combination of generative AI and various AI models allows businesses to scale up their operations efficiently. Thanks to generative AI’s ability to generate high-quality outputs quickly and accurately, businesses can easily add new offerings and expand into new markets without sacrificing quality.

For example, e-commerce platforms can use generative AI to generate content, useful data, and other things they may need at scale. These platforms can also automate inventory management, customer service, and fulfillment to accommodate their local and international customers.

Final thoughts on Generative AI Implementation

An effective deployment of generative AI is a complex process that requires careful planning, experimentation, and iteration. However, the results are totally worth it. By following the above guide, you’ll be able to build a robust generative AI solution capable of generating realistic samples all on its own. With generative AI capabilities, you’ll be able to improve your processes, reduce operational costs, and gain a competitive edge in your industry.

Generative AI Implementation – FAQ

What is Generative AI?

Generative AI is a type of artificial intelligence technology capable of creating new and realistic content in response to prompts. It can generate various types of content such as text, audio, images, videos, software code, product designs, and synthetic data.

What are some key concepts in Generative AI?

Some key concepts in Generative AI include Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Transformers. VAEs focus on mapping input data into a lower-dimensional latent space to generate new samples. GANs consist of a generator and a discriminator to create realistic outputs. Transformers use self-attention mechanisms for tasks like natural language processing and image generation.

How can I implement Generative AI into my business?

Implementing Generative AI involves several steps:

  • Understand the problem and identify potential use cases.
  • Prototype the Generative AI model, experimenting with algorithms and datasets.
  • Select appropriate algorithms and preprocess data.
  • Set up a proper development environment.
  • Develop the Generative AI model, optimizing for performance.
  • Deploy the model in a productive environment, ensuring security and reliability.

Why is Generative AI essential for businesses?

Generative AI offers benefits such as enhanced creativity, hyper-personalization, better decision-making, improved customer service, increased efficiency, and scalability. It can automate tasks, personalize customer experiences, and generate insights for informed decision-making, leading to operational efficiency and competitive advantages.

What are the main challenges in implementing generative AI?

  • Data Quality and Quantity

Generative AI models require massive amounts of high-quality training data to function accurately and generate relevant outputs. Many organizations struggle to obtain sufficient domain-specific data that represents their products or services.

  • Biases and Ethical Concerns

There are risks of generative AI models producing biased, discriminatory or offensive content if trained on biased datasets or without proper safeguards. Ensuring ethical and responsible use of AI is crucial to maintain trust.

  • Intellectual Property Issues

Training data may contain copyrighted or proprietary information, raising legal concerns over ownership and usage rights of the AI-generated content.

  • Lack of Transparency and Explainability

The decision-making processes of deep learning models are often opaque “black boxes”, lacking transparency and making it difficult to explain their outputs. This lack of explainability hinders trust and adoption.

This article is an updated version of the publication from Nov 3, 2023.


[1] Economic Potential of generative AI. URL: Accessed October 11, 2023
[2] What Is VAE. URL:  Accessed October 11, 2023
[3] Top 25 Generative AI Use Cases in 2023. URL: Accessed October 11, 2023
[4] What is Data Labeling. URL:, Accessed October 11, 2023
[5] Data Splitting. URL: Accessed October 11, 2023
[6] Feature Extraction in Image processing. URL:, Accessed October 11, 2023


Generative AI