Generative AI
Generative AI refers to AI systems that create new content—text, images, code, audio, video—rather than classifying or analyzing existing data. ChatGPT, DALL-E, Midjourney, and Stable Diffusion are all generative AI. The underlying models learn statistical patterns from training data and produce new outputs that follow those patterns. This is a meaningful shift from the previous decade of AI, which was dominated by discriminative models (spam filters, recommendation engines, fraud detectors) that sorted things into categories. Generative AI crossed into mainstream adoption faster than any technology since the smartphone, reaching 100 million users in two months. But the speed of adoption has outrun the infrastructure for using it well. Most organizations are still in the experimentation phase—running pilots, debating policies, and trying to figure out where generative AI creates value that justifies the cost, the risk, and the organizational change required to use it at scale.
Related terms:
Temperature
Temperature is a parameter controlling a language model’s randomness: at 0 it always picks the most probable next token for deterministic, reliable output, at 1 it samples more broadly for varied, creative results, and above 1 it becomes increasingly random. Choosing the right temperature (e.g., 0 for consistent data extraction or 0.7–0.9 for brainstorming) balances reliability and diversity.
AI Governance
AI governance comprises the policies, processes, and technical controls that organizations use to manage the risks of AI deployment, from deciding appropriate use cases and evaluating models to ensuring accountability, data privacy, bias mitigation, and regulatory compliance under frameworks like the EU AI Act. Without clear governance, “shadow AI” can proliferate as employees use unmonitored AI tools with no oversight or audit trails.
Token
In large language models, a token is the basic unit of text—usually chunks of three to four characters—that the model reads and generates. Since API costs, context windows, and rate limits are all measured in tokens, understanding tokenization is essential for controlling prompt length, cost, and model behavior.