Describe your AI problem and get routed to the right skill with a ready-to-use prompt. Use when you are not sure which ai- skill to use, want help picking the right approach, or just want to describe what you need in plain language. Also use this when someone…
Legacy reference for dspy.Assert and dspy.Suggest, both removed in DSPy 3.x - use dspy.Refine or dspy.BestOfN instead. Use when maintaining a legacy DSPy 2.x codebase that uses dspy.Assert or dspy.Suggest, or migrating from Assert/Suggest to DSPy 3.x. Also…
Runs any DSPy module N times and returns the highest-scoring output via a reward function using dspy.BestOfN. Use when output quality varies across runs and you want to sample multiple completions and pick the best — trading latency for reliability on…
Jointly optimize prompts and model weights using dspy.BetterTogether for maximum accuracy. Use when you have already tried prompt-only optimization and want the next level — jointly tuning prompts and model weights for maximum quality. Common scenarios - you…
Automatically generate and select few-shot demonstrations for a DSPy program from labeled data using dspy.BootstrapFewShot - the simplest and fastest optimizer. Use when you have 50+ labeled examples and want a quick accuracy boost as your first optimization…
Generates training data from a teacher model and fine-tunes student model weights using dspy.BootstrapFinetune. Use when you need maximum quality from a smaller/cheaper model — distilling GPT-4 quality into a cheaper model, generating training data from a…
Optimize few-shot demos for a DSPy program by searching over multiple candidate demo sets using dspy.BootstrapFewShotWithRandomSearch. Use when basic BootstrapFewShot is not enough and you want better results at the cost of more LM calls. Common scenarios -…
Use when the task benefits from intermediate reasoning before producing an answer — multi-step logic, analysis, math, or complex classification where direct prediction fails. Common scenarios - classification tasks where the model needs to reason about edge…