Top 5 RAG Based AI Tools | Top Retrieval-Augmented Generation (RAG) AI Tools

Hi everyone, welcome back. In this video, we will look at the top 5 RAG-based AI tools for beginners. RAG tools help AI answer from documents, websites, files, and knowledge bases. So instead of only using general AI memory, these tools retrieve useful information first and then generate an answer. The first tool is Google Notebook LM. Notebook LM is one of the best tools to understand RAG as a beginner. You can upload PDFs, Google Docs, notes, web links, and other study materials. Then you can ask questions from those sources. Notebook LM gives answers based on your uploaded material, which makes it very useful for students, teachers, researchers, and content creators. For example, you can upload a course PDF and ask, explain this chapter in simple words. So Notebook LM is best for learning from documents and creating study notes. The second tool is ChatGPT with file search or custom GPT knowledge. ChatGPT can work like a RAG-based assistant when you upload files or create a custom GPT with knowledge. For example, you can upload documents, PDFs, or course notes and ask questions from them. In the OpenAI API, file search allows the model to search uploaded files using semantic and keyword search before generating an answer. This is useful when you want to build your own document Q&A assistant, support chatbot, or internal knowledge assistant. So ChatGPT is best for personal productivity, custom GPTs, and developer-friendly RAG applications. The third tool is Microsoft Copilot Studio. Copilot Studio is useful for businesses that want to create AI agents using company knowledge. You can connect knowledge sources like websites, documents, SharePoint, Dataverse, and enterprise data. Then, the agent can use those knowledge sources to generate grounded answers. For example, a company can build an HR assistant that answers from our policies or a customer support bot that answers from product documentation. So Microsoft Copilot Studio is best for enterprise RAG and business chatbots. The fourth tool is Cloud Projects. Cloud Projects lets you create a project and add knowledge to it. You can upload documents, text, code, and other files to the project knowledge base. Cloud then uses that project knowledge as context during chats. This is useful for long-running work, writing projects, coding projects, research, and business documentation. For example, you can create one project for a course, upload course notes, and ask Cloud to help create summaries, scripts, quizzes, and explanations from that material. So Cloud Projects is best for document-based workflows and project-specific knowledge. The fifth tool is DiFi. DiFi is a platform for building AI applications, including RAG-based chatbots. It provides knowledge base features where you can manage documents, chunks, and retrieval. You can build an AI chatbot that answers from your uploaded documents or external knowledge base. DiFi is more technical than NotebookLM, but it is very useful if you want to build real AI apps. For example, you can create a chatbot for product manuals, company FAQs, or internal support documents. So DiFi is best for building custom RAG applications without starting completely from scratch. Now let's quickly summarize. Use NotebookLM for learning from PDFs, notes, and study materials. Use ChatGPT with file search or custom GPT knowledge for personal assistance and developer-friendly RAG apps. Use Microsoft Copilot Studio for enterprise agents and business knowledge. Use Cloud Projects for project-based document understanding. Use DiFi for building custom RAG chatbots and AI applications. So the simple idea is this. If you want AI to answer from trusted documents, these RAG-based tools can help you turn your files and knowledge bases into useful AI assistants. I hope this video helped you understand the top RAG-based AI tools clearly. Thanks for watching and I will see you in the next lecture.