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It depends on the optimizer. BootstrapFewShot works with as few as 10-20 examples for simple tasks. MIPROv2 and GEPA benefit from 50-200+ examples. The DSPy team recommends starting with 20-50 high-quality labeled examples, running an initial optimization, evaluating results on a held-out set, and then deciding whether to annotate more data based on the quality gap.
Yes. After optimization, you can call program.inspect() or use dspy.inspect_history(n=1) to see the last prompts sent to the LLM, and access compiled prompts through each module's demos and instructions attributes. You can manually edit these or use them as starting points for further optimization.
LangChain is an orchestration toolkit where you manually write prompts and chain LLM calls together — it gives fine-grained control over prompt details and has a much larger ecosystem of integrations and tools. DSPy takes a fundamentally different approach: you define what you want (via signatures and metrics) and let optimizers figure out how to prompt the model. Choose LangChain for rapid prototyping with manual control; choose DSPy for systematic, measurable quality optimization.
Yes. DSPy supports any model through its LM abstraction backed by LiteLLM — OpenAI, Anthropic, Google Gemini, Databricks, Together.ai, Ollama, vLLM, HuggingFace Transformers, and any OpenAI-compatible endpoint. Local models via Ollama or vLLM work seamlessly, and DSPy's optimizers are particularly valuable for squeezing maximum performance out of smaller open-source models.
DSPy is fully free and open-source under the MIT license, with no paid tier, no usage limits, and no commercial restrictions. The only costs are the LLM API calls you make during optimization and inference, which depend on your chosen provider and usage volume.
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