WHAT WE LEARNED FROM A YEAR OF BUILDING WITH LLMS (PART I)

Summary from the TLDR newsletter

While the barrier to entry for building AI products has lowered, creating something effective beyond a demo remains a deceptively difficult endeavor. This series of articles identifies crucial lessons and methodologies for developing products based on large language models gathered by people who have been building real-world applications on top of ML systems over the past year. It is organized into three sections: tactical, operational, and strategic. This first part dives into the tactical nuts and bolts of working with large language models and shares best practices and common pitfalls around prompting, setting up retrieval-augmented generation, applying flow engineering, and evaluation and monitoring.