Classical supervised learning trains models on labeled historical data to learn a mapping from input features to a target outcome (classification or regression). Algorithms such as logistic regression, random forests, gradient boosting, and support vector machines infer statistical relationships between structured features and labels. Once trained and validated, these models generalize to new, unseen records to predict probabilities, classes, or numeric values. They are best suited to well-defined, tabular problems with clear business metrics and sufficient labeled data.

Generative AI is a family of models that learn the statistical structure of data (text, images, audio, code, etc.) and then sample from that learned distribution to create new content. These models are typically built with deep neural architectures such as transformers, diffusion models, and GANs, and can be conditioned on prompts, examples, or structured inputs. In applications, generative models are often combined with retrieval systems, tools, and business logic to ground outputs in real data and workflows. Effective use requires careful attention to safety, reliability, governance, and alignment with domain constraints.

Conversational RAG (Retrieval-Augmented Generation) extends basic RAG to multi-turn dialogue, where each response is grounded in external knowledge while preserving conversational context. It combines conversation history, user profile, and task state to build richer retrieval queries and select relevant documents at every turn. The model then generates answers that reference both retrieved content and prior messages, enabling follow-up questions, refinements, and long-running tasks. This makes it suitable for chatbots that need memory, document navigation, and iterative problem solving.