Executive RAG Cookbook

As an enterprise developer, it's essential to understand Retrieval Augmented Generation (RAG) and its potential to unlock a whole new set of applications that can drive innovation and efficiency across your organization.

RAG represents a powerful approach to enhancing the capabilities of Large Language Models (LLMs), enabling them to provide more accurate and contextually relevant responses by leveraging knowledge sources external to the LLM. This can significantly improve the performance of applications that require up-to-date information, confidential data, or domain-specific expertise.

RAG unlocks this world of promise. Companies are building, buying, and deploying these systems in the form of four primary different types of Generative AI (GenAI) applications:

1. Question Answering Application: an application that provides a response to user questions based on the organization’s data, in any number of supported languages, utilizing LLMs to generate these summarizations while providing citations to the source documents in the response.
2. Embedded RAG: embedding the RAG flow into existing enterprise applications, allowing these applications to enhance the functionality of these applications with advanced search or GenAI functionality.
3. AI Assistant: AI assistants extend question-answering applications by providing a chat-based interface, including full context of past conversations and user context. Using Agentic RAG, AI assistants gain access to additional capabilities, including better understanding of the users’ questions, the ability to utilize tools to better address the users’ needs, and a capability to handle more complex requests.
4. AI Agent: AI agents go a step further than AI assistants, allowing them to safely act on the user’s behalf to achieve a desired outcome by identifying, prioritizing, and executing an action plan using tools that can take action (e.g. send an email, or create a calendar invite). AI agents can interact with the end-user directly, like AI assistants do, but can also be stand-alone agents that regularly perform some function on behalf of the user.