A human's guide to generative AI

Generative AI is everywhere. The claims are massive, but the reality is more nuanced.

This guide separates fact from fiction. You'll learn what generative AI actually is, where it excels, and where other tools do the job better. You'll discover why copying text into a public chatbot can leak your company's secrets, why an LLM sounds confident but often fabricates facts and why building your own AI model costs millions and isn't necessary.

Most importantly, you'll find out which problems generative AI solves and which ones it doesn't.

Get the facts on gen AI

We walk you through the fundamentals, the practical applications, and the pitfalls you need to avoid. Whether you're evaluating AI for your organization or trying to understand the technology itself, this guide gives you the grounding you need to make informed decisions.

• The basics: How AI, machine learning, and generative AI differ. What makes generative AI different from everything before it.
• When to use it: Generative AI shines at text generation, code writing, synthetic data creation, and design automation, but there are situations where other tools do a better job.
• The real risks: Copyright infringement, hallucinations, data privacy breaches, bias, and environmental costs. These aren't theoretical concerns, they're happening now at companies like Samsung and Amazon.
• How to access it safely: Four options exist, from consumer tools to building your own foundation model. We explain the trade-offs for each approach.
• Industry-specific use cases: Banking, manufacturing, aerospace and defense, and biotech all have different opportunities and constraints when implementing generative AI.

Download the full ebook and get the knowledge to make smart decisions about when, how, and whether to use generative AI.