What We Learned from a Year of Building with LLMs

Ready to build real-world applications with large language models? With the pace of improvements over the past year, LLMs have become good enough for use in real-world applications. LLMs are also broadly accessible, allowing practitioners besides ML engineers and scientists to build intelligence into their products.

In this report, six experts in AI and machine learning present crucial, yet often neglected, ML lessons and methodologies essential for developing products based on LLMs. Awareness of these concepts can give you a competitive advantage against most others in the field.

Over the past year, authors Eugene Yan, Brian Bischof, Charles Frye, Hamel Husain, Jason Liu, and Shreya Shankar have been busy testing and refining these methodologies by building real-world applications on top of LLMs. In this report, they have distilled these lessons for the benefit of the community.