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.
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 for the first optimization pass. Move to BootstrapFewShotWithRandomSearch when enough examples are available to search multiple demo sets.

Prompt Search

Use dspy-miprov2-optimizer for instruction and demonstration search. Install its optional dependency first:

pip install -U "dspy[optuna]>=3.2.1,<3.3"

Reflective Optimization

Use dspy-gepa-reflective when failures can be described with actionable text. Use dspy-simba-optimizer for a smaller mini-batch introspective loop with numeric metrics.

Prompt Plus Weight Optimization

Use dspy-better-together 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