Generative AI refers to a class of artificial intelligence systems that can create new content, such as text, images, code, audio, or video, based on patterns learned from existing data. These models do not just classify or analyse, they generate original output that resembles human-created content.
How Generative AI Works
Generative AI systems are built on large neural networks trained on massive datasets. One popular type is the transformer model, which powers tools like ChatGPT, DALL·E, and Bard. These models learn relationships between inputs and outputs by processing billions of examples during training.
Once trained, the model uses probabilities to predict the next word, pixel, or frame. Given a prompt or input, it creates content that aligns with the structure and style of the data it has seen. Some models are fine-tuned for specific tasks like writing code, generating marketing copy, or designing graphics. Others are open-ended and adapt to many use cases.
Where It Is Used
Generative AI is being applied in a wide range of enterprise functions. Marketing teams use it to draft content. Legal teams use it to summarise documents. Developers use it to write and review code. Designers use it to generate images and layouts. CX teams use it to power chatbots and automated replies.
Startups and enterprises alike are exploring generative AI for customer engagement, knowledge management, and multilingual content generation. Industries such as finance, healthcare, and retail are testing use cases with measurable impact.
What You Should Know
Generative AI works best when guided by clear inputs and constraints. It can produce inaccurate or biased outputs if not governed properly. Use cases that involve sensitive data, decision-making, or compliance should include human oversight.
