dspy-optimizer-selection
This skill guides selection among eleven DSPy optimizers based on data volume, budget constraints, and optimization targets. Use it when choosing between methods like LabeledFewShot for baselines, BootstrapFewShot for moderate datasets, MIPROv2 for instruction search, or BootstrapFinetune for weight distillation, with a decision matrix matching specific needs to the most cost-effective optimizer.
git clone --depth 1 https://github.com/OmidZamani/dspy-skills /tmp/dspy-optimizer-selection && cp -r /tmp/dspy-optimizer-selection/skills/dspy-optimizer-selection ~/.claude/skills/dspy-optimizer-selectionSKILL.md
# DSPy Optimizer Selection ## Goal Choose the smallest DSPy optimizer that matches the data, budget, and artifact being tuned. Establish a baseline before compiling anything. ## Selection Matrix | Need | Start with | Notes | |------|------------|-------| | Include a few labeled examples | `dspy.LabeledFewShot` | Random labeled demos; useful as a baseline | | About 10 examples | `dspy.BootstrapFewShot` | Teacher-generated demos with metric filtering | | 50+ examples and stronger demo search | `dspy.BootstrapFewShotWithRandomSearch` | Searches multiple demo sets; alias: `dspy.BootstrapRS` | | Per-input nearest demos | `dspy.KNNFewShot` | Retrieves nearby examples before bootstrapping | | Instruction-only hill climbing | `dspy.COPRO` | Coordinate ascent over instructions | | Instruction and demo search | `dspy.MIPROv2` | Bayesian search; install `dspy[optuna]` | | Mini-batch introspective rules or demos | `dspy.SIMBA` | Uses output variability and self-reflection | | Rich textual feedback and trace reflection | `dspy.GEPA` | Metric must accept five arguments | | Distill prompts into model weights | `dspy.BootstrapFinetune` | Requires a fine-tunable LM and `set_lm()` | | Combine candidate programs | `dspy.Ensemble` | Trades inference cost for robustness | | Sequence prompt and weight optimization | `dspy.BetterTogether` | Meta-optimizer for configurable optimizer chains | ## Workflow 1. Split data into train and validation sets. 2. Evaluate the uncompiled program with [dspy-evaluation-suite](../dspy-evaluation-suite/SKILL.md). 3. Start with the least expensive optimizer that matches the need. 4. Save the compiled program and compare it against the baseline. 5. Escalate only when the measured gain justifies extra LM calls, fine-tuning, or inference cost. ## Common Paths ### Fast Demo Optimization Use [dspy-bootstrap-fewshot](../dspy-bootstrap-fewshot/SKILL.md) for the first optimization pass. Move to `BootstrapFewShotWithRandomSearch` when enough examples are available to search multiple demo sets. ### Prompt Search Use [dspy-miprov2-optimizer](../dspy-miprov2-optimizer/SKILL.md) for instruction and demonstration search. Install its optional dependency first: ```bash pip install -U "dspy[optuna]>=3.2.1,<3.3" ``` ### Reflective Optimization Use [dspy-gepa-reflective](../dspy-gepa-reflective/SKILL.md) when failures can be described with actionable text. Use [dspy-simba-optimizer](../dspy-simba-optimizer/SKILL.md) for a smaller mini-batch introspective loop with numeric metrics. ### Prompt Plus Weight Optimization Use [dspy-better-together](../dspy-better-together/SKILL.md) when a fine-tunable LM is available and prompt optimization alone has plateaued. ## Best Practices 1. Keep a held-out validation set. 2. Track optimization cost and inference cost separately. 3. Use reproducible seeds where supported. 4. Avoid claiming one optimizer is universally best; compare measured results. 5. Save intermediate candidates for expensive runs. ## Official Documentation - **Optimizer guide**: https://dspy.ai/learn/optimization/optimizers/ - **Optimizer API index**: https://dspy.ai/api/optimizers/ - **DSPy releases**: https://github.com/stanfordnlp/dspy/releases
Use this skill when you need to QA audit and fix a plugin skill file. Provides a methodology for verifying skill content against official documentation, fixing issues in-place, and producing verification reports.
Use for DSPy adapter selection, JSONAdapter, XMLAdapter, ChatAdapter, native function calling, structured outputs, and multimodal inputs like dspy.Image or dspy.Audio.
Use for composing DSPy modules with Ensemble, MultiChainComparison, ensemble voting, sequential pipelines, and multi-program workflows.
Use for BetterTogether, prompt plus weight optimization, fine-tuning sequences, and strategy chains like p -> w -> p.
Use for BootstrapFewShot, bootstrapped demonstrations, teacher-model demos, and low-data DSPy prompt optimization.
Use for creating custom DSPy modules, extending dspy.Module, reusable components, stateful modules, serialization, and module testing.
Use for debugging DSPy programs, inspect_history, tracing LLM calls, custom callbacks, observability, monitoring, and cost tracking.
Use for DSPy retrieval with dspy.Embedder, dspy.Embeddings, FAISS indexes, semantic search, and local or hosted embedding models.