Key takeaways
- Generative AI creates new content; traditional AI mostly classifies, predicts or scores.
- Large language models are the generative models behind most AI writing tools.
- It is strong at drafts, summaries and rewrites, and weak at guaranteeing that what it says is true.
- In sales and marketing, generated text is a starting point that a person checks and edits.
How generative AI works
Generative AI works by learning the patterns of a huge body of examples and then producing new output that fits those patterns, one piece at a time.
A text model predicts a likely next word, adds it and repeats, so each sentence is built from what usually follows in writing like it. Many image models start from random noise and refine it towards a picture that matches the prompt. Neither looks up an answer; both compose one, which is why the output can be original and wrong at the same time.
Generative AI vs traditional AI
Traditional AI decides between known options, while generative AI produces something that did not exist before.
A lead-scoring model outputs a number, and a spam filter outputs yes or no. A generative model outputs a paragraph, an image or a voice. Both usually rest on deep learning; the difference is the job, choosing versus creating. Many tools now combine them, for example judging a reply's intent and then drafting an answer.
How generative AI relates to large language models
Large language models are the generative AI models that work with text, and they power most chat assistants and writing tools.
Generative AI also covers models for images, audio, video and code. When people say generative AI at work, they usually mean a language model in a chat window, sometimes with access to company information or tools.
Generative AI in sales: an example
A typical use of generative AI in sales is drafting a follow-up email from a call summary.
In an illustrative case, a seller asks for three versions of a follow-up: one short, one that restates the agreed next steps and one that answers the pricing question raised on the call. The model writes all three. The seller picks one, corrects a date, removes a promise the team cannot keep and sends it. The drafting was quick; the judgement stayed with the person.
What generative AI does well and what it cannot promise
Generative AI does well at language work with a person checking the result, and it cannot promise that its output is accurate, current or within your rules.
Researchers call fluent but false output hallucination. Giving the model your own source material, and reviewing anything that names a price, a date or a commitment, keeps the risk manageable.
- Does well: first drafts, summaries of long threads, rewrites in a set tone, variations to compare.
- Cannot promise: facts and figures, arithmetic, knowledge of events after its training, or compliance with a policy it was not given.
Where Pebbles Ai fits
Common questions
What does gen AI mean?
Gen AI is short for generative AI: models that create text, images, audio or code from a prompt.
Is generative AI the same as a chatbot?
No. A chatbot is an interface for conversation; many modern chatbots run on a generative language model, but older ones followed scripted rules and generated nothing new.
Can generative AI write sales emails on its own?
It can draft them, but a person should check facts, names and any commitment before sending. Generated text reads well whether or not it is accurate.
About this page. Written by the Pebbles Ai team for people who run sales, marketing and growth. Last updated .
