Optimizing LLMs in Insurance with DSPy: Jeronim Morina
Feb 16, 2025 · 19:29
Jeronim Morina from AXA Germany argues that AI engineers must stop relying on manual prompt tuning and instead adopt DSPy's automated optimization combined with first-principles thinking. He explains how his team built a customer-facing insurance chatbot by first crafting clear problem definitions, annotating evaluation data, and avoiding data leakage. After initial struggles with fragile prompts and overly complex error-handling code, they modularized their system into DSPy modules and created custom metrics for German text. Morina emphasizes that tools like DSPy require a steep learning curve and a solid baseline of hand-written prompts and basic evaluations before optimization. The episode calls for engineers to focus on real-world impact, measure everything, and break down problems into discrete, optimizable steps.
