Unlock the potential of Retrieval-Augmented Generation (RAG) as you delve into this comprehensive course designed to equip you with the skills to create efficient and reliable applications. Embrace the journey to mastering RAG systems, exploring advanced concepts like Agentic RAG systems, and gain the methodologies to develop diverse applications across many fields.
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 primary 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 materials.
Course Topics
Throughout this course, students will engage with Flowise AI, simplifying the development of complex agent workflows. Here's an overview of the main topics covered:
1. Introduction to RAG
- Basic principles of Retrieval-Augmented Generation
- Advantages over traditional generation methods
- Key application areas
2. RAG Architecture
- Technical structure of RAG systems
- Data chunking methods
- Embedding models
- Vector databases and semantic search
- Interaction between retriever and generator components
3. Creating Simple RAG Systems
- Practical creation of an initial RAG system
- Developing a personalized tutor using RAG
4. Developing a RAG Chat Assistant
- Application of RAG in chatbots, catering to popular business scenarios
- Creating an online chat assistant for customer support
- Setting up document storage and integration with RAG
- Enhancing search quality with techniques like query expansion
5. Advanced RAG
- Implementing enhanced prompting techniques including:
- Tool calling
- Chain-of-Thought prompting (CoT)
- Prompt chaining
- Developing a complex RAG application integrating LLM concepts
6. Agentic RAG Systems
- Integrating AI agents into RAG systems with a modern approach
- Utilizing function calling to expand RAG capabilities
- Developing an Agent RAG application for interactions with external tools:
- Calculator
- Logical reasoning tool
- Chain of LLM calls
7. Deployment of RAG Applications
- Creating an online application with sharing features
- Applying best practices to enhance RAG performance
Who Will Benefit from This Course
This course is ideal for professionals in artificial intelligence, data analytics, business process automation, customer support, research, and programming, as well as anyone interested in learning about Retrieval-Augmented Generation.
Companies Whose Employees Have Taken Our Courses
Our training participants include employees from prestigious 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 completing the course, students will be proficient in developing and implementing RAG applications that effectively combine information retrieval and answer generation to tackle various business challenges.
