MIPRO is a prompt optimization framework that creates and refines the LLM prompts for improved accuracy and consistency . MIPRO uses an LLM to generate additional instructions to add to the prompt and then selects few-shot examples that illustrate successful executions of the given task, optimizing the selection of both using a PyTorch powered ML framework [Opsahl-Ong, 2024]. MIPRO can bootstrap examples from training data and dynamically generate instruction candidates to provide structured, task-specific guidance . Through Bayesian optimization, MIPRO iteratively identifies the optimal combination of examples and instructions, evaluated against a user-generated quantitative metric. This prompt refinement reduces hallucinations by ensuring that the LLM has a clear and relevant framework for understanding underlying data, without the need for creating or fine-tuning a model for a specialized application .