The architecture of today's LLM applications

The blog post discusses the process and architecture involved in building applications using Large Language Models (LLMs), focusing on empowering developers to exploit these models' capabilities. It highlights five essential steps for developing LLM apps, including identifying a focused problem, selecting the right model, customizing the model through techniques like in-context learning and fine-tuning, setting up the app's architecture with tools for user input and prompt optimization, and conducting online evaluations to assess real-time performance. The text also explores the emerging architecture of LLM applications, illustrated through a hypothetical example of a user interacting with an LLM-powered assistant to resolve an internet connectivity issue. Additionally, it emphasizes the importance of efficient and responsible AI tooling, such as caching, content filtering, and telemetry services, and offers insights into real-world applications of LLMs in various fields like climate science, medical guidance, language learning, and ecommerce.