Prompt engineering gets messy fast. What starts as a simple instruction can turn into endless tweaking, context adjustments, and rework when model behavior changes. @DSPyOSS approaches that differently by treating LLM apps more like software you can optimize instead of prompts you endlessly babysit.
Building LLM Applications with DSPy shows how to use DSPy's contract-based Python framework to optimize context, evaluate prompt effectiveness, and adapt as models drift.
New in the Manning Early Access Program and 50% off through June 3rd: https://hubs.la/Q04hnHsw0