What We Learned from a Year of Building with LLMs (Part I)

What We Learned from a Year of Building with LLMs (Part I)

摘要

It’s an exciting time to build with large language models (LLMs). Over the past year, LLMs have become “good enough” for real-world applications. The pace of improvements in LLMs, coupled with a parade of demos on social media, will fuel an estimated $200B investment in AI by 2025. LLMs are also broadly accessible, allowing everyone, not just ML engineers and scientists, to build intelligence into their products. While the barrier to entry for building AI products has been lowered, creating those effective beyond a demo remains a deceptively difficult endeavor.

We’ve identified some crucial, yet often neglected, lessons and methodologies informed by machine learning that are essential for developing products based on LLMs. Awareness of these concepts can give you a competitive advantage against most others in the field without requiring ML expertise! Over the past year, the six of us have been building real-world applications on top of LLMs. We realized that there was a need to distill these lessons in one place for the benefit of the community.