Introduction to Generative AI
Generative AI produces new text, images, audio or other content from learned patterns and an input. A classifier might label a message as spam; a generative model might draft a reply to it.
#Where it fits
Artificial intelligence is the broad field. Machine learning learns patterns from data. Deep learning uses multi-layer neural networks. Many modern generative systems use deep learning, but these terms do not mean the same thing.
A language model generates tokens conditioned on context. An image model uses a different generation process. Neither should be understood as copying a verified fact database into the output.
#Discriminative and generative tasks
| Task | Typical output |
|---|---|
| Classify a support request | A category such as billing |
| Predict a numeric value | An estimated quantity |
| Generate a support reply | New text conditioned on the request |
| Generate an illustration | New pixels conditioned on instructions |
A product can combine these stages. For example, classify a ticket, retrieve an approved policy and draft a reply for review.
#Know what the output proves
Fluent text can contain false details. A convincing image can depict an event that never happened. Check important claims against sources and label generated examples clearly.
A useful first exercise is to summarize a short document, then compare every claim with the original. Record missing qualifications, invented facts and useful compression. This teaches the difference between a readable output and a trustworthy one.
Continue with large language models and responsible AI.
