Activeloop, Towards AI, and the Intel Disruptor Initiative are excited to collaborate to bring Gen AI 360: Foundational Model Certification Course for aspiring Generative AI professionals, executives, and enthusiasts of tomorrow.
Following the success of our "LangChain & Vector Databases In Production" and "Training and Fine-tuning LLMs for Production” courses, we're excited to welcome you to the third part of the series: “Retrieval Augmented Generation for Production with LlamaIndex and LangChain.”
In this course, you'll learn how to build on RAG techniques learned in the first Langchain and Vector DBs in Production course. Apart from a primer on novel LangChain RAG frameworks, you will learn advanced RAG techniques with LlamaIndex and how to build RAG agents and RAG evaluation systems. This course will guide you on the optimal methods and practices for getting RAG production-ready with plenty of applied industry project examples. Let's get started!
The “Retrieval Augmented Generation for Production with LlamaIndex and LangChain” course provides the theoretical knowledge and practical skills necessary to build advanced RAG products.
Many human tasks across various industries can be assisted with AI by combining LLMs, prompting, RAG, and fine-tuning workflows.
We are huge fans of RAG because it helps with
reducing hallucinations by limiting the LLM to answer based on existing documentation,
helping with explainability, error checking, and copyright issues by clearly referencing its sources for each comment,
giving private/specific or more up-to-date data to the LLM,
and not relying on black box LLM training/fine tuning for what the models know and has memorized.
We touched upon basic RAG in our first Langchain and Vector DBs course, but building more advanced and reliable products requires more complex techniques and iterations of the model.
The 'Retrieval Augmented Generation for Production with LlamaIndex and LangChain' course aims to provide you with the theoretical knowledge and practical skills necessary to develop products and applications centered on RAG.
A fundamental pillar of our course is the focus on hands-on learning. Real-world application and experimentation are crucial for a deep understanding and effective use of RAG techniques.
In this course, you will move beyond basic RAG apps, develop these applications with more advanced techniques, build RAG agents, and evaluate the performance of RAG systems.
Whether planning to build a chat with data application for your organization or just learning how to leverage Generative AI in various industries, this course is for you. The course addresses critical issues such as reducing hallucinations in AI outputs, enhancing explainability, addressing copyright concerns, and offering more tailored, up-to-date data inputs. We go beyond basic RAG applications, equipping you with the skills to create more complex, reliable products with tools like LangChain, LlamaIndex, and Deep Memory. Emphasizing hands-on learning, this course is a gateway to mastering advanced RAG techniques and applications in real-world scenarios. Please note that prior knowledge of coding and Python is a prerequisite.
You will start by learning the basic RAG tools, such as loading, indexing, storing, and querying in both Langchain and LlamaIndex. We’ll also demystify the two libraries to help you select the right one when working with RAG or other LLM applications. You will then move towards more advanced RAG techniques aimed at surfacing and using more relevant information from the dataset. We cover techniques such as Query expansion, Transformation reranking, recursive retrieval, optimization, and production tips and techniques with LlamaIndex. We also introduce how better embedding management through Activeloop’s Deep Memory can be used to improve accuracy. We then progress to the exciting stuff: learning how to build RAG agents in Langchain and Llamaindex, an introduction to OpenAI assistants and some other tools & models that can be used in RAG products. We conclude with a summary of RAG evaluation techniques in LlamaIndex together with an introduction to Langsmith in Langchain.