I think it's important for JavaScript developers to really use these tools and build them into your applications as well. I hope that this course will help you with that. When building applications using large language models, there are common steps that many developers use. LangChain.js make these steps much easier for JavaScript developers. For example, if you're building a retrieval augmented generation or RAG application, you'd have to choose an LLM to perform your task, figure out how to retrieve relevant texts to fill the element's context, tune the prompt and maybe also pause the element's text output into something more structured for the downstream steps of your application. Tools that help you connect these steps and also enable you to quickly tune this workflow, for example, swapping out one LLM for another, these tools are called orchestrators. LangChain is a very popular open source orchestrator for LLM applications and will help you to build your LLM applications much more quickly. The instructor for this course is Jacob Lee, founding software engineer at LangChain and lead maintainer for the open source LangChain.js Jacob has worked with many developers to help them integrate into the web and mobile applications. Thank you, Andrew. And I'm really excited about what you'll be able to learn in this course. You will learn about a number of elements which are common in LLM applications, like data loaders, which make it convenient to pull data from common sources, such as PDFs, websites, databases, and more, to augment the LLMs generation. There's parsers. LLMs operate with natural language, while programming languages operate with formatted data. Parsers extract and format natural language output to create structured forms for your downstream code to process. There's prompts, which are used to provide the LLM context. Models, which provide an abstraction layer on top of specific LLMs, so that you can write applications that are themselves not vendor-specific. And then there are other modules to support RAGs, such as text splitters and integrations with vector stores. You'll also use the LangChain Expression Language, or LCEL for short, to easily compose complex chains of these modules. Quite a few people had contributed to make this course possible. We're grateful to Harrison Chase, founder and CEO of LangChain, as well as Nuno Campos, also from LangChain. And on the dblend.ai side, Geoff Ladwig and Esmaell Gargari also contributed to this course. So lots of great stuff to learn here. And after this course, I hope you really built some really cool LLM-based applications in JavaScript. Jacob, what's with the parrot in the LangChain logo? You know, Andrew, I'm not really sure, but it's become part of the uniform.