Question 1

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

Accepted Answer

Traditional machine learning usually predicts or classifies (for example, "is this email spam?" or "what will demand be next week?"). Generative AI creates new content (for example, writing a draft email, generating an image, or producing code). Both learn patterns from data, but generative AI is optimized to produce outputs that look like human-created content.

Question 2

When should I use Generative AI?

Accepted Answer

Use generative AI when you need to create or transform content quickly, such as drafting text, summarizing documents, generating code, creating marketing variations, building chat assistants, or extracting structured data from unstructured text. It’s especially useful when the output can be reviewed by a human or validated by rules/tests. Avoid using it as the only decision-maker for high-stakes tasks (medical, legal, financial approvals) unless you have strong controls, human oversight, and validation.

Question 3

How much does Generative AI cost?

Accepted Answer

Costs typically depend on (1) the model you choose, (2) how many tokens you send/receive (text length), (3) latency/throughput needs, and (4) whether you fine-tune or use retrieval-augmented generation (RAG). Managed services usually charge per request or per token for inference, plus any extra costs for embeddings, vector databases, storage, and networking. Larger models and longer prompts/responses cost more, and adding safety filters, monitoring, or higher availability can increase total cost.

Generative AI

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