dspy-expert

A DSPy Signature defines the input and expected output for your program. You establish a minimal baseline by wiring this Signature into a simple Module to create a working reference across train/dev/test splits.

An evaluation harness in DSPy runs deterministic testing using train/dev/test splits and metric functions. It provides traceable metrics and failure logging to ensure robust prompt program optimization and debugging.

To build a minimal DSPy baseline, frame your task by defining the input, expected output, and evaluation criteria. Then implement a small Module with a Signature to establish a working reference for evaluation.

Yes, DSPy supports local OpenAI-compatible endpoints for tool integration. You can wire modules to these endpoints to enable RAG patterns and incremental prompt improvements for production-grade workflows.

DSPy program evaluation requires deterministic metrics and proper error handling. If results vary, ensure your evaluation harness uses fixed train/dev/test splits and includes logging to trace metric failures.

You extend DSPy modules by building on the minimal Signature baseline. The framework enables easy extension to tool use, RAG patterns, and multi-stage prompts while maintaining deterministic evaluation and logging.