The blog post discusses the process of building an open-source web research assistant powered by Tavily, focusing on engineering decisions involved in creating applications that connect Large Language Models (LLMs) to external knowledge sources using Retrieval Augmented Generation (RAG). The article details the challenges and decisions in the retrieval step, such as whether to always perform lookups, handle follow-up questions, and manage multiple search terms or lookup steps. It also outlines the use of the Tavily Search API for retrieving snippets and highlights the importance of generating search queries to improve response accuracy. For the augmented generation step, the article explains the choice of using GPT-3.5-Turbo for its cost-effectiveness and speed, as well as the decisions around prompt design and providing sourced responses to enhance reliability and allow deeper exploration of information. The blog post aims to provide insights into the tradeoffs of engineering decisions in RAG applications and offers a starting point for developers with a shared code repository.