Generative AI initiates with a prompt, which can take the form of text, an image, a video, a design, musical notes, or any input processable by the AI system. Utilizing various algorithms, the AI then generates new content in response to the provided prompt. This content spans essays, problem solutions, or realistic creations derived from images or audio. In the earlier stages of generative AI, submitting data required using an API or a complex process. Developers had to engage with specialized tools and write applications in languages such as Python.

Presently, pioneers in generative AI are enhancing user experiences. Users can articulate requests in plain language, and after an initial response, they have the ability to customize results by providing feedback on elements like style and tone. This simplifies and refines the content generation process.

• Scale of Compute Infrastructure: Generative models require substantial compute infrastructure and investment for efficient training.
  
• Sampling Speed: The scale of models may introduce latency, impacting quick response times in interactive applications.
• Lack of High-Quality Data: Generative models depend on high-quality, unbiased data, and some domains face data scarcity.
• Data Licenses: Obtaining commercial licenses for datasets poses challenges, adding complexity to model training.