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Workflow Automation with AI embeds models such as LLMs, OCR, and ML classifiers into orchestrated, multi-step business workflows. It uses triggers, AI-powered tasks, human-in-the-loop approvals, and system integrations to execute processes end-to-end with minimal manual effort. Traditional workflow or orchestration engines coordinate the sequence, while AI steps handle perception, understanding, and decision-making. Monitoring, governance, and exception handling ensure reliability, compliance, and auditability in production environments.

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.