DAIR.AI

OVERVIEW

This course focuses on building effective and robust retrieval augmented generation (RAG) applications. Students will learn the fundamental building blocks for building RAG systems and the best practices for creating advanced RAG applications. The course also covers advanced concepts such as Agentic RAG systems.

After completing the course, students will have a solid understanding of RAG systems and acquire best practices for building advanced RAG applications in different domains.

PREREQUISITES

If you are not familiar with advanced prompting techniques for LLMs, we strongly recommend completing both the Introduction to Prompt Engineering and Advanced Prompt Engineering courses (also available to all Pro members).

The main tool you will use is Flowise AI (a popular no-code tool to build advanced RAG-based workflows and agentic workflows), therefore, no programming is required. More details and instructions about how to access and install Flowise AI are provided in the course.

TOPICS

Throughout the course, students will utilize Flowise AI, a no-code tool that simplifies the process of developing complex agent-based workflows. 

Outcomes and key concepts covered in the course include:

• RAG Introduction: Learn the fundamentals of RAG and its essential components. Understand why RAG is an important advancement in AI and discover common applications where RAG provides advantages over traditional approaches.
• RAG Architecture: Explore the technical architecture of RAG systems, covering chunking, embedding models, vector databases, and semantic search fundamentals. Students will explore how retrievers and generators work together while learning key enhancements that optimize RAG performance.
• Building Naive RAG Systems: Students will apply the fundamentals to build their first RAG application from scratch. You will build a personalized tutor using RAG.
• Build a RAG Chat Assistant: Chat assistant is one of the most common enterprise use cases where RAG is applied. Students will learn how to create a document store from scratch, build the chat assistant with RAG, and apply common techniques like query expansion to improve results. You will build a RAG-powered customer service chatbot for an online website.
• Advanced RAG: Students will implement an advanced RAG system and apply more advanced prompting techniques like tool calling, chain-of-thought prompting (CoT), and prompt chaining to improve reliability and response quality. You will build a complex RAG solution that unifies core ideas used for building with LLMs.
• Agentic RAG: Includes one of the most recent and advanced ways to build agentic-based RAG systems. Students will learn about function calling and how agents can integrate with a RAG system to extend its capabilities and improve user experience. You will build an Agent RAG application that interacts with external tools such as a calculator, a reasoning chain tool, and an LLM chain tool to complete customer orders.
• Deploy RAG Apps: Students will take all the learnings from the course and build a shareable online RAG application to receive feedback. You will also learn more advanced tips for how to continue improving your RAG apps. 

Here's a subset of companies whose employees have benefitted from our courses:

Accrete, Airbnb, Alston & Bird, Amazon, Apple, Arm, Asana, Bank of America, Belong For Me, Biogen, Brilliant, Carebound, CDM, CentralReach, Centric Software, Chime, Coinbase, Digital Green, DoHQ, Elekta, Fidelity Investments, Fivecast, Fulcrum Labs, Google, Guru, Gretel, Harrison Insights, Intel, Intuit, Jina AI, JPMorgan Chase & Co, Khan Academy, KnowBe4, Lawyer.com, LinkedIn, LionSentry, MagmaLabs, MasterClass, Meta, Metopio, Microsoft, Moneta Health, Oracle, OpenAI, Rechat AI, RingCentral, Salesforce, Scale AI, Scribd, Space-O Technologies, Sun Life, TD Bank, TELUS Corporations, Trilogy, TTEC Digital, UniCredit, VaxCalc Labs, Vendr, Walmart, Wolfram Alpha, Zemoso Technologies, Zeplin 

Reach out to [email protected] for any questions and team/student discounts.