The era of large language models (LLMs) is here, with rapid advancements making them increasingly accessible for real-world applications. With an estimated $200B investment in AI by 2025, LLMs are set to revolutionize product development for everyone, not just experts. This guide, crafted by a diverse team of experienced individuals, provides valuable insights into building efficient LLM applications. The tactical section covers critical components like prompting, retrieval-augmented generation, and structured inputs and outputs. Surprisingly, techniques like chain-of-thought prompting and retrieval-augmented generation are under-discussed. Embrace these tactics and learn how to optimize your LLM workflows for success.
https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-i/