Build a Prompt Learning Loop - SallyAnn DeLucia & Fuad Ali, Arize
Jan 6, 2026 · 52:08
SallyAnn DeLucia and Fuad Ali from Arize present prompt learning, a method to continuously improve AI prompts by using feedback from evals and human annotations. They argue that agent failures often stem from weak instructions rather than weak models, and that adding rules to system prompts can yield 15% improvements on SWE-Bench without fine-tuning or architecture changes. The talk compares prompt learning to GEBA, noting it achieves better results in fewer loops by leveraging text explanations. A case study shows Kline's performance improved from 30% to 45% on SWE-Bench-Lite after optimizing prompts. The workshop demonstrates building an optimization loop that ingests input-output pairs, evaluates them, and iteratively refines the system prompt using OpenAI.
