Insights
RAG and fine-tuning are two different ways to make a general AI model useful for your specific data and needs. Most enterprise use cases are better served by RAG; fine-tuning solves a narrower, different problem.
Read articleRAG (retrieval-augmented generation) grounds an AI model's answers in passages retrieved from your own documents at query time, rather than relying solely on what the model learned during training.
Read articleAgentic AI refers to AI systems, often multiple coordinated agents, capable of planning and executing multi-step work across an organization's systems. For enterprises, that means automation that can span departments and tools rather than a single task.
Read articleAI agents automate business processes by combining natural-language understanding with direct, permissioned access to the systems where work actually happens, turning multi-step manual workflows into a single automated interaction.
Read articleAI agents are systems that can reason about a goal, plan the steps to reach it, and take real action by calling tools and APIs, not just answer questions in conversation.
Read article