What is Generative AI?

Unlike traditional AI models that predict a label or rank a list, generative AI learns patterns from large datasets and uses those patterns to compose original output. The output is shaped by the input prompt, any context provided, and constraints set by the developer. In commerce and customer-facing applications, generative AI is most often used to produce natural-language responses, product copy, summaries, and recommendations.

How does generative ai work?

• A user submits a prompt or question.
• The model processes the prompt against patterns learned during training, along with any additional context supplied at runtime (such as product catalog data or policy documents).
• The model generates a response token by token, predicting what text best fits the prompt and context.
• Developers add guardrails - filtering, grounding, and output validation - to keep responses accurate and on-brand.

Why does it matter?

For ecommerce operators and auto dealerships, generative AI means shoppers or buyers can ask open-ended questions and receive a direct, conversational answer rather than scrolling through static FAQ pages or waiting for a human agent. It reduces support load, accelerates the path to purchase, and lets a small team serve far more visitors without proportionally more staff. Operators who deploy it well report measurable lifts in conversion and reductions in pre-sale inquiry volume.

Nobi applies generative AI to answer shopper and buyer questions in natural language, grounded in the merchant's own catalog and content so responses stay accurate and on-brand.

Frequently asked questions

Is generative AI the same as a large language model? Not exactly. A large language model (LLM) is one type of generative AI focused on text. Generative AI is a broader category that also covers image generators, audio synthesis, and code generation tools.

How do businesses keep generative AI responses accurate? Accuracy is controlled by grounding the model in verified source material - product feeds, policy documents, knowledge bases - so it draws on real data rather than general training knowledge. Output filters and human review loops catch errors before they reach customers.

What is the difference between generative AI and traditional search? Traditional search returns a ranked list of existing documents matching a query. Generative AI composes a direct answer, synthesizing information from multiple sources into a single coherent response tailored to the specific question asked.