Few Shot Example Synthesis And Selection

via “few-shot example synthesis and selection”

Stanford framework that replaces manual prompting with automatically optimized LLM programs.

Unique: Automatically selects examples from training data based on metric-driven feedback, rather than relying on manual curation or random sampling. Advanced optimizers like GEPA can synthesize new examples using reflective reasoning, generating demonstrations that target specific failure modes.

vs others: More sophisticated than random example selection and more scalable than manual curation, DSPy's example synthesis integrates with the optimization loop to learn examples that maximize task-specific metrics.