Learning path · Programmatic Prompting · 29
DSPy
Framework treating LLM pipelines as optimizable programs—signatures, modules, and teleprompters search for better prompts and weights.
Why it matters
- Moves prompt tuning from artisanal guessing toward measurable compilation.
- Pairs naturally with RAG and agent module graphs.
- Reported industry case studies show strong gains on structured tasks.
Key ideas
- Programs not strings
- Automatic prompt search
- Composable modules
DSPy reframes prompts as parameters in a program you can optimize. Define signatures for inputs and outputs, compose retrieval or tool modules, then let teleprompters propose improved instructions or demonstrations against a metric. It shines when you have labeled or LLM-judge evals and repetitive pipeline structure. Start small—one signature, one metric—before compiling entire agent graphs. Invest in metric design before optimizer runs—a teleprompter will happily overfit a vague score that rewards verbosity over correctness. Ship only after eval gates pass on representative production failures.
Updated 2026-08-09 · Full learning path