What is Generative AI and How Can You Build It
Boosting your knowledge and integrating AI concepts in your world
Generative AI was all the craze last year and continues to dominate conversations on technology and the future. This article will break down what Generative AI is and help you better understand it in the context of the current cultural landscape.
Generative AI refers to a class of artificial intelligence systems designed to create new content—such as text, images, audio, video, or code—based on patterns learned from existing data. Unlike traditional AI models that primarily analyze or classify, generative AI models are creators. They generate human-like outputs, making them powerful tools in industries ranging from software development to design, marketing, and education.
What are some examples of generative AI?
The most popular example is ChatGPT, which generates conversational text; DALL·E, which creates images from textual descriptions; and GitHub Copilot, which helps developers write code. These tools are transforming how we work—streamlining workflows, sparking creativity, and expanding access to complex capabilities once limited to specialists.
How do you build generative AI models?
Similarly to building a chatbot (which you can read more about in my article here), AI generative models follow this same process but go a step further by focusing on multiple types of output beyond text.
At its core, building a generative AI system involves several steps:
Data Collection: Start with a large, diverse dataset relevant to the content you want your model to generate—be it natural language, images, or code.
Model Selection: Choose a foundational architecture such as a transformer (used in models like GPT or BERT). Open-source frameworks like Hugging Face Transformers or OpenAI's APIs are popular starting points.
Training & Fine-tuning: Train your model on the collected data. This process involves adjusting millions (or billions) of parameters to help the model learn patterns. Fine-tuning can tailor a model to specific industries or use cases, such as tech consulting or healthcare.
Deployment: Once trained, your model can be integrated into applications via APIs, user interfaces, or platforms—making it accessible to real users.
For women in tech, especially in consulting roles, understanding generative AI can be a career accelerator, opening doors to innovation, entrepreneurship, and leadership.
📚 Here are some articles you can keep reading to learn more about generative AI applications: