BootstrapFewShot Teleprompter

A teleprompter that bootstraps demonstrations by having a teacher model generate predictions on training examples and selecting successful ones as demonstrations. This is DSPy's foundational optimization approach.

The optimizer:

1. Starts with optional labeled demonstrations from the training set

2. Uses a teacher model to generate predictions on remaining examples

3. Evaluates predictions using the provided metric

4. Selects top-scoring predictions as bootstrapped demonstrations

5. Optionally runs multiple rounds, updating the teacher with new demos

Usage

BootstrapFewShot(
  metric = NULL,
  metric_threshold = NULL,
  max_errors = 5L,
  max_bootstrapped_demos = 4L,
  max_labeled_demos = 16L,
  max_rounds = 1L,
  teacher_settings = NULL,
  seed = NULL,
  log_dir = NULL
)

Arguments

metric

A metric function for evaluating predictions (required).

metric_threshold

Minimum score for a demo to be accepted. If NULL, accepts any successful prediction. Default is NULL.

max_errors

Maximum number of errors allowed during optimization. Default is 5.

max_bootstrapped_demos

Maximum number of bootstrapped demonstrations to include. Default is 4.

max_labeled_demos

Maximum number of labeled demonstrations from the training set. Default is 16.

max_rounds

Number of bootstrap rounds to perform. Default is 1.

teacher_settings

List of settings for the teacher model, such as temperature or model. If NULL, defaults to list(temperature = 0.7).

seed

Random seed for reproducibility. Default is NULL.

log_dir

Directory for trial logging. Default is NULL.

Examples

if (FALSE) { # \dontrun{
# Create a BootstrapFewShot teleprompter
tp <- BootstrapFewShot(
  metric = metric_exact_match(field = "answer"),
  max_bootstrapped_demos = 4L,
  max_labeled_demos = 8L
)

# Compile a module
compiled <- compile(tp, qa_module, trainset, .llm = llm)
} # }