DSPy: The End of Prompt Engineering - Kevin Madura, AlixPartners
Jan 8, 2026 · 1:13:13
Kevin Madura of AlixPartners argues that building robust enterprise AI applications requires shifting from brittle prompt engineering to programming with LLMs using DSPy, a declarative framework that treats prompts as implementation details optimized by the system. He demonstrates how typed interfaces (Signatures) and modular logic (Modules) allow developers to focus on control flow while deferring implementation to the LLM, with Adapters controlling prompt formats (e.g., JSON vs. BAML) to improve performance by 5-10%. The talk's core is Optimizers (like MIPRO and JEPA), which automatically tune prompts by learning from data, shown improving a time entry corrector from 86% to 89% accuracy. Real-world examples include routing files by type (SEC filings vs. contracts), using a 'poor man's RAG' with attachments for multimodal documents, and a boundary detector that segments legal documents from images. Madura emphasizes that DSPy enables transferability across models (e.g., GPT-4.1 to GPT-4.1 Nano) and addresses cost concerns by allowing offline optimization to reduce LLM calls.
