Generative AI Solution: A Complete Guide

Generative AI tech stack: An overview

According to a KPMG research report, nearly 72% of US CEOs state that generative AI is a top investment in the global market despite the current uncertain economic conditions. CB Insights concluded that 2023 saw the highest investment in generative AI startups, making this a breakout year. Equity funding topped $21.8B in 426 deals this year.

Several generative AI companies are already securing funding and achieving high valuations in venture capital. A recent research report revealed Jasper, a copywriting assistant, raised $125 million this year at a $1.5 billion valuation. Again, Hugging Face and Stability AI raised $100 million and $101 million with valuations of $2 billion and $1 billion, respectively.

Comparing these achievements to OpenAI, Microsoft secured over $1 billion of its $25 billion investment. This suggests that generative AI companies have attracted investors’ attention despite the market downturn.

What is Generative AI?

A powerful Generative AI, or GenAI usually uses machine learning to design and create new content. You should know that machine learning is also a form of artificial intelligence that learns from data patterns first without taking the human direction.

It is rich with a lot of complex data which is often analyzed to provide AI app development solutions. To put it simply, GenAI can learn what things are and then use this information to create new things.

At least no one was aware of commercial uses for GenAI until a few days after ChatGPT’s explosive discovery in 2022. At this point, open AI users may find GenAI models novel. However, this technology has a wide range of applications with many benefits.

How to Build a Generative AI Solution?

Building a generative AI product must follow several key steps, from data collection to deployment and monitoring. Here’s a step-by-step of How to build a generative AI solution:

1. Define Your Objective

Before focusing on the technical aspects of this solution, it is very important to clearly understand how you want to use the Generative AI idea and what you want to achieve from it. If you have a clear purpose behind using it, it will guide your decisions throughout the development process.

2. Choose the Right Model

Which model you want to choose depends on your objective. Here are some popular models for generative AI mentioned below:

• GPT (Generative Pre-trained Transformer): Perfect solution for text generation.
• GANs (Generative Adversarial Networks): create realistic images excellently and efficiently.
• VAEs (Variational Autoencoders): efficiently generate the new data based on the input data.
• DALL-E and CLIP: generate the images from textual descriptions.

3. Gather and Prepare Your Data

You need large datasets in the training period for Generative AI models. At the outset, you should know that the performance of the model can be significantly affected by the quality and quantity of data. Users are, therefore, advised to collect a diverse and comprehensive dataset relevant to the project. You must source data from different channels to cover all your essential aspects.

After collecting the data, you must remove irrelevant or low-quality entries so that your dataset can be robust and reliable. Finally, format it according to your needs and make it suitable for training. This is an effective preparation step that is crucial for the generative AI model to generate high-quality outputs.

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4. Prototyping

To test your AI model’s core functionalities, it is important to build a simplified version. With this prototype, you can visualize the AI system’s potentiality, identify difficulties and challenges, and redesign the design if you need to. Before investing the time and resources, you can make adjustments early in the process according to the prototype.

5. Train Your Model

After preparing data and prototypes, your next step is training the generative AI model, allowing the model to learn through iteration by feeding it with pre-processed data.

6. Evaluate and Fine-Tune Your Model

Using the metrics relevant to your objective, you must evaluate your model’s performance in your training. It is an important step involving model testing with a validation dataset. It allows you to see how well Generative AI can generate new content.

7. Develop and Integrate the Solution

To set up the development environment, you must use the necessary tools and libraries. You have to focus on the below-mentioned aspects:

• Data Storage
• Data Processing Frameworks
• Parallelization and Containerization
• Cloud Computing
• Microservices Architecture
• Caching and Load Balancing
• Error Handling and Documentation
• Model Optimization

8. Testing and Validation

To perform thorough testing such as performance, unit, A/B, and user testing, you can hire an efficient “QA tester.” It allows you to ensure the solution meets accuracy and quality standards.

9. Deployment

The next step is to convert your model from development to practical use. This step also requires ensuring the model is compatible with the required hardware to handle increased usage. Test extensively to integrate the model with the current system.

These steps effectively build a robust Generative AI solution that meets your needs.

Types of Generative AI Models

There are different types of generative AI business applications. Each design is used for specific tasks. This diversity ensures that this broad model of generative AI can be used effectively in different applications, industries, and businesses. 

Generative Adversarial Networks (GANs)

Generative Adversarial Networks, or GANs, are the pioneer AI model introduced in 2014. Consisting of two parts named a generator and a discriminator, GANs have a high rate of successfully generating realistic content and finding applications such as art creation, cybersecurity, and data synthesis successful for AI training.

Large Language Models or LLM

Large Language Models (LLMs) are notable generative AI models that have proven their efficiency in Natural Language Processing (NLP). LLM can be grouped into OpenAI’s GPT and Google’s BERT-similar groups. Trained on extensive text data, these model structures of generative AI apps efficiently perform tasks such as translating any data, summarizing, and producing human-like text.

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Diffusion Models

Diffusion models can be called a recent innovation in generative AI models that have proven their performance in producing high-quality images and videos. Unlike traditional methods, diffusion models can improve image quality by refining the probability distribution of pixels.

Their use in industries like photorealistic rendering, digital art creation, fashion design, and medical imaging is increasing daily.

Variational Autoencoders (VAEs)

Variational autoencoders (VAEs) essentially combine elements of autoencoders and probabilistic models. They present input data in a simple format and then create new and innovative data models based on the presentation.

Apart from generating output, this model also performs important tasks like image generation, data augmentation, and anomaly detection.

Transformer-based Models

Generative AI’s model is powered by natural language processing and machine learning. So it can easily perform language translation, text generation, data retrieval, etc.

If you are able to select the perfect model for your Generative AI solution, it helps you to stand out from the crowd.