What is Generative AI?

Generative AI (GenAI) represents a fundamental shift in what AI systems can do. Traditional AI was primarily discriminative — it learned to distinguish between categories (is this email spam or not? is this tumour benign or malignant?). Generative AI learns the underlying patterns in data well enough to produce new, original examples of that data.

The most transformative form of generative AI is the Large Language Model (LLM), which powers products like ChatGPT, Claude, Gemini, and Copilot. But generative AI also includes image generation models (Midjourney, DALL·E, Stable Diffusion), code generation tools (GitHub Copilot), audio synthesis (ElevenLabs), and video generation (Sora, Runway).

For corporate teams, generative AI primarily means LLM-powered tools — AI that can write, summarise, analyse, translate, and reason about business documents, emails, reports, code, and customer interactions.

Most commercial generative AI systems are built on transformer neural networks trained on massive datasets. For text, the model learns to predict the next token in a sequence — given billions of examples, it learns patterns sophisticated enough to produce coherent, contextual language.

The training process has two phases: pre-training (on a vast general corpus) and alignment (fine-tuning on human-rated examples to make the model helpful, safe, and accurate). This second phase is why modern LLMs are dramatically more useful than raw pre-trained models.

For images, diffusion models are the dominant approach: they learn to reverse a process that gradually adds noise to images, enabling them to generate realistic images from text descriptions by "denoising" random noise guided by the text prompt.