Question 1

What's the difference between Generative AI and machine learning?

Accepted Answer

Machine learning is a broad set of techniques where systems learn patterns from data to make predictions or decisions (like classifying spam or forecasting demand). Generative AI is a subset that focuses on creating new content—such as text, images, audio, or code—by learning patterns from large datasets and generating outputs that look human-made.

Question 2

When should I use Generative AI?

Accepted Answer

Use it when you need to produce or transform content at scale or improve knowledge work. Common cases include drafting and summarizing documents, customer support chat, searching and answering questions over internal knowledge (often with retrieval-augmented generation), generating code or tests, creating marketing copy, and generating or editing images. Avoid using it for fully automated decisions in high-stakes scenarios (medical, legal, safety-critical) without strong human review, validation, and governance.

Question 3

How much does Generative AI cost?

Accepted Answer

Costs usually depend on (1) the model you choose, (2) how many tokens you send and receive (for text models), (3) image/audio generation volume, (4) throughput and latency requirements, and (5) whether you fine-tune or host dedicated capacity. Many cloud services charge per request or per token, and costs can increase with long prompts, large context windows, high traffic, or advanced models. You should estimate usage with typical prompt/response sizes, add monitoring, and set budgets/quotas to control spend.

Generative AI

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