Terms - AI ( PhD )

LangChain is an open-source programming framework that enables developers to construct applications utilizing large language models (LLMs). These applications, known as Agents, connect the LLMs to external databases, APIs, and tools allowing them to function autonomously. It is essential to understand that LangChain is not the agent itself, but rather the operating system or framework that empowers the agent. The agent is the fully functional AI system that results from the integration of LangChain with LLMs.

Generative AI, often referred to as GenAI, is a tool that creates text, code, or answers based solely on the input provided. It functions on the principle of taking a prompt (input) and generating text (output). Generative AI does not have the capability to take action independently, browse live websites, or click buttons. Its primary function revolves around generating text based on the given input.

Retrieval Augmented Generation (RAG) is a system design utilized within Generative AI. It involves designing the system in such a way that it can efficiently retrieve relevant information from a large dataset to generate accurate and contextually relevant responses. Essentially, RAG enhances the capabilities of Generative AI by providing it with a vast repository of information to draw upon while generating responses.

An AI Agent, also known as Agentic AI, is an autonomous assistant given a final goal. Unlike traditional Generative AI that simply provides answers, an AI Agent has access to various tools, such as the internet, calculators, or software applications. This allows the AI Agent to engage in a loop of thinking, deciding, and taking action.

The agent takes a step, evaluates the result, decides on the next action, rectifies any mistakes, and continues this process until the final goal is achieved. The primary focus of Agentic AI is on thinking, deciding, and taking action, rather than just generating text based on the input.