ai-kickoff

Scaffolding a DSPy AI feature project is done by running a kickoff flow that generates an opinionated skeleton with standard files like main.py, program.py, and metrics.py. This ensures a consistent structure for data loading, model definition, and evaluation workflows.

The standard project structure for a DSPy application includes a main entry point alongside standard module files like data.py, metrics.py, and evaluate.py. This opinionated layout provides ready-to-fill templates for data loading, program definition, and evaluation.

Yes, you need Python to set up a DSPy program skeleton. The scaffolding process requires Python, the DSPy framework, and a preferred language model provider to populate the requirements.txt file and initialize the environment correctly.

Yes, you can use this scaffold for AI classification and data extraction features. The generated project structure is ideal for building new AI-powered applications, providing templates for data loading, evaluation, and decision automation workflows.

The best way to bootstrap a DSPy workflow without ad-hoc setups is to use an opinionated scaffolding tool. It generates a complete project layout with standard files for program definition and evaluation, ensuring a consistent and scalable foundation from the start.