Hand-Picked Demonstrations with dspy.LabeledFewShot

Guide the user through using dspy.LabeledFewShot -- the simplest DSPy optimizer. It takes labeled examples you provide and attaches them as few-shot demonstrations to your program's predictors. No bootstrapping, no metric, no LM calls during optimization.

What is LabeledFewShot

dspy.LabeledFewShot is an optimizer that takes a set of labeled training examples and injects them directly as few-shot demonstrations into every predictor in your DSPy program.

• No metric required -- unlike other optimizers, it does not evaluate or filter examples
• No LM calls during compilation -- it just copies your examples into the prompt
• Deterministic -- uses a fixed random seed (0) for reproducible example selection
• Fast -- compilation is instant because there is no search or bootstrapping step

Under the hood, compile() creates a copy of your program, iterates over each predictor, and assigns up to k examples from your training set as that predictor's demos.

When to use LabeledFewShot