Building (and Breaking) WebLangChain

The blog post outlines the development of an open-source web research assistant utilizing Retrieval Augmented Generation (RAG) to integrate large language models (LLMs) with real-time internet data. It details the engineering decisions involved in creating such applications, including whether to always perform lookups, how to handle follow-up questions, and how to manage search queries. The post emphasizes the importance of understanding these decisions' trade-offs, such as speed versus comprehensiveness and reliability versus flexibility in handling complex queries. It discusses the use of Tavily, a search API, to efficiently obtain reliable information, and the post also provides the source code to encourage further development of LLM-backed applications. Additionally, the blog highlights the use of GPT-3.5-Turbo for its cost-effectiveness and speed, and it explains the decision-making process behind query formulation and response generation, including the provision of source citations for verification.

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