What Is Generative AI?

What does generative AI mean?

Generative AI is artificial intelligence capable of generating new content, such as text or images, in response to a submitted prompt by learning from a large reference database of examples. IBM describes generative AI models as relying on deep learning techniques that identify and encode patterns and relationships in huge amounts of data, then use that learned pattern to respond with relevant, novel content rather than retrieving a stored, pre-written answer. Tools using this technology include chatbots such as ChatGPT and Claude, and image generators such as Midjourney, all built on the same underlying premise: predict and produce new content that resembles the patterns found in the training data.

The distinction from traditional software is direct. Traditional software behaves exactly as coded. Generative AI produces probabilistic output, meaning the same prompt can produce a slightly different answer each time.

Why generative AI matters for product managers

Builders Camp's Foundations of AI bootcamp exists specifically to give product managers a working mental model of generative AI and agents, "so you can separate hype from reality," as its own description puts it, understanding core concepts, what is feasible today, and the trade-offs behind the scenes. This matters because a PM does not need to build a model from scratch to make good decisions about generative AI features. They need to understand what the technology reliably does well, what it does not, and where the gap between the two creates product risk.

Builders Camp's AI Product Management bootcamp names the same underlying shift directly: the job of a PM has not changed at its core, valuable, usable, buildable, viable, but the deliverable has moved from a static spec toward the environment a generative model actually operates inside, its evaluation harness, its guardrails, its business design.

How generative AI is used in practice

Foundations of AI's certification quiz tests a distinction that matters directly for how a PM scopes a generative AI feature: the difference between a model, a workflow, and an agent, where an agent "combines models and workflows with memory and reasoning" while a model alone "handles core generation tasks." A PM proposing an AI feature that only needs to generate a single piece of content, a draft email, a summary, is working purely at the model layer. A PM proposing a feature that needs to check multiple sources, take an action, and adapt based on what it finds is designing something closer to an agent, with all the added guardrail and evaluation needs that implies.

How Builders Camp teaches generative AI

Foundations of AI covers generative AI across its entire curriculum, from a foundational mental model through audio, image, and video AI applications across the product lifecycle, research, discovery, prioritization, delivery, go-to-market, and support. Builders Camp's AI Product Management bootcamp builds on that foundation with a framework specifically for scoping, evaluating, and iterating on generative AI features once they move from concept to a real product decision.

See best AI tools for product managers for a practical starting point, or large language model for the specific technology behind most text-based generative AI. See the Foundations of AI bootcamp for the complete curriculum.