Introduction to RAG

Course Requirements

• If you are not familiar with advanced methods of prompt writing for LLM, it is recommended to first complete the courses "Introduction to Prompt Engineering" and "Advanced Prompt Engineering".
• The main tool for the course is Flowise AI, a popular no-code platform for building complex RAG and agent workflows. No programming is required.
• Detailed instructions for installing and accessing Flowise AI are provided in the course.

Course Topics

Throughout the course, students will work with Flowise AI, which will simplify the development of complex agent workflows.

Main topics of the course:

1. Introduction to RAG

• Basic principles of Retrieval-Augmented Generation
• Advantages of RAG over traditional generation methods
• Main areas of application

2. RAG Architecture

• Technical structure of RAG systems
• Data chunking methods
• Embedding models
• Vector databases and semantic search
• Interaction between the retriever and generator parts of RAG

3. Creating Simple RAG Systems

• Practical creation of the first RAG system
• Development of a personalized tutor using RAG

4. Developing a RAG Chat Assistant

• Application of RAG in chatbots - one of the most in-demand business scenarios
• Creation of an online chat assistant for customer support
• Setup of document storage and integration with RAG
• Methods to enhance search quality, such as query expansion

5. Advanced RAG

• Implementation of enhanced prompting techniques
  • Tool calling
  • Chain-of-Thought prompting (CoT)
  • Prompt chaining
• Development of a complex RAG application combining key concepts of working with LLM

6. Agentic RAG Systems

• Modern approach to integrating AI agents into RAG systems
• Utilizing function calling to extend RAG capabilities
• Development of an Agent RAG application interacting with external tools:
  • Calculator
  • Logical reasoning tool
  • Chain of LLM calls

7. Deployment of RAG Applications

• Creation of an online application with sharing capabilities
• Best practices for enhancing RAG performance

Who Will Benefit from This Course

This course is suitable for professionals working in the fields of artificial intelligence, data analytics, business process automation, customer support, research, and programming, as well as for anyone looking to learn about Retrieval-Augmented Generation.

Companies Whose Employees Have Taken Our Courses

Training participants include employees from companies such as Google, OpenAI, Microsoft, Meta, JPMorgan Chase & Co, Amazon, Salesforce, Airbnb, Apple, Intel, Khan Academy, Oracle, LinkedIn, Walmart, Fidelity Investments, and many others.

Upon completion of the course, students will be able to develop and implement RAG applications that can effectively combine information retrieval and answer generation for various business tasks.