DSPy Bootstrap Few-Shot Optimizer FAQs

Question 1

Is a teacher model required for bootstrapping?

Accepted Answer

While you can use the same model, using a stronger teacher model (like GPT-4o) to generate demonstrations for a student model (like GPT-4o-mini) typically yields better results.

Question 2

How many training examples do I need for this skill?

Accepted Answer

This skill is most effective when you have a small dataset of 10-50 labeled examples, making it ideal for early-stage optimization.

Question 3

What are reasoning traces in DSPy?

Accepted Answer

Reasoning traces are intermediate steps or 'Chain-of-Thought' logs that the teacher model generates before providing a final answer, which helps the student model learn the logic behind a task.

Question 4

Can I use this for production environments?

Accepted Answer

Yes, the skill includes instructions for saving the compiled program's state to a JSON file, which can then be loaded in production without re-running the optimization.

Question 5

How does this differ from the MIPROv2 optimizer?

Accepted Answer

BootstrapFewShot is designed for small datasets and quick compute, whereas MIPROv2 is intended for larger datasets (200+ examples) and more extensive hyperparameter exploration.

DSPy Bootstrap Few-Shot Optimizer

Key Features

Use Cases

DSPy Bootstrap Few-Shot Optimizer

Key Features

Use Cases