GenAI & LLM foundations
What Is Generative AI?
A clear, non-technical definition of generative AI and why it is different from earlier AI systems.
Generative AI refers to systems that create new content — text, images, audio, code, video — rather than just classifying or predicting from fixed categories. Instead of sorting an email into "spam" or "not spam," a generative model can write the email itself.
This shift, powered mainly by large language models and diffusion models, is what unlocked the current wave of AI products: chatbots, coding assistants, image generators, and the agents built on top of them.
Key takeaways
From prediction to creation
Earlier AI systems were mostly predictive: given an input, output a label, a score, or a ranked list from a fixed set of possibilities. Generative AI instead produces open-ended output — a paragraph, an image, a block of code — that did not exist as a fixed option beforehand.
This is a fundamentally different capability, and it is why generative AI enabled products like conversational assistants that traditional predictive models never could.
What generative AI is built on
Text generation is powered by large language models trained on huge amounts of text to predict likely next words, which in aggregate produces coherent, useful writing. Image and audio generation use related architectures like diffusion models.
You do not need to understand the training process to use these systems well, but understanding that they are pattern-based predictors — not reasoning engines with true understanding — helps you use them appropriately.
Where it fits in real products
On its own, a generative model can hallucinate, forget instructions, or produce inconsistent output. That is why production AI products wrap generative AI with retrieval (to ground answers in real data), tools (to take real actions), and evaluation (to catch failures).
Generative AI is the creative engine; the rest of AI engineering is what makes that engine dependable.
Put this into practice