What Problem Is DSPy Solving? (www.gojiberries.io)

This note reframes prompt engineering as a fixed‑budget best‑arm identification problem and evaluates DSPy against that lens. DSPy gives a strong programming model—typed module signatures, composable pipelines, parsing helpers, versioned configs—plus components to generate instruction variants and curate few‑shot examples and a Bayesian proposer (e.g., MIPROv2). That workflow is productive for “forgiving” tasks (generic summaries, light QA, routine classification) where broad sampling and LLM‑paraphrased instructions reliably find good regions, and typed I/O prevents many errors. The critique is that DSPy’s defaults underplay allocation, constraints, variance control, structure, and governance—critical when you have a strict evaluation cap and production constraints. Practical fixes proposed: treat prompt parts (instruction, constraints, reasoning scaffold, schema, demonstrations) as first‑class and hold critical blocks fixed; apply constraint gates (parseability, token budgets, P95/P99 latency, safety) before deep evaluation; adopt racing schedules (successive halving or harmonic successive‑rejects) and survivor bandit rules (Thompson sampling, LUCB) for allocation; use paired, stratified batches, fixed decoding/retrieval, and interval estimates to reduce variance; prefer dueling‑bandit models for pairwise preferences; add novelty filters, sealed holdouts, and failure mining into regression tests. These changes keep DSPy’s ergonomics while aligning search and evaluation with tight budgets and deployment realities.