Building (and Breaking) WebLangChain

This blog post discusses the process of building a web research assistant powered by Tavily using Retrieval Augmented Generation (RAG). RAG involves two steps: retrieval and augmented generation. The author walks through various engineering decisions involved in creating such an application, including whether to always look something up, how to handle follow-up questions, and the choice of search engine. They also discuss the pros and cons of allowing multiple lookup steps and using a single or multiple LLM for generating responses. The post concludes by summarizing the retrieval and generation logic used in their application and encourages readers to build their own RAG applications.