PromptWizard: Task-Aware Prompt Optimization Framework

In fact, feedback-based prompt optimization methods are already quite mature, including approaches like APO, PromptAgent, and PE2. PromptWizard is not the first feedback-driven optimization method, and the authors overlooked related works of this nature in their writing. Therefore, I recommend the authors add related work of this type.

Comparison with Feedback-based prompt optimization approaches:

Thank you for this thoughtful comment. We recognize the importance of explicitly addressing comparisons with similar feedback-based prompt optimization approaches such as APO, PromptAgent, and PROMST. Below is a detailed articulation of how PromptWizard (PW) stands apart from these methods, highlighting its unique contributions and advantages:

1. APO vs. PW:
   APO employs a gradient-based feedback loop where an initial prompt is refined iteratively using bandit algorithms. While this approach introduces feedback for prompt refinement, it is inherently limited by a lack of diverse exploration. APO does not mutate the prompt through different reasoning styles or perspectives, which can restrict its ability to handle complex tasks, especially when the initial prompt is suboptimal. This is critical for challenging datasets like GSM8k or AQUARAT.

a. In contrast, PW leverages a feedback-driven critique-and-synthesis mechanism that incorporates diverse thinking styles for mutation, ensuring a wide exploration space before refining prompts. This not only accelerates convergence but also enhances robustness in scenarios where the initial prompt is weak.

b. Additionally, APO’s reliance on gradients and its iterative nature result in higher computational costs. Our experiments show that APO typically requires 2–3 hours for prompt optimization, with significantly higher API calls and token usage compared to PW, which achieves optimized prompts in just 20–30 minutes.

2. PromptAgent vs. PW:
   PromptAgent introduces Monte Carlo Tree Search (MCTS) for exploring multiple prompt variations and uses an external LLM for feedback at every branch of the tree. While this enhances exploration, feedback is not used to guide the exploration directly but instead evaluates multiple independent variations. This results in inefficiency, as the system evaluates a large number of branches without iterative refinement driving convergence.

a. PW, on the other hand, uses feedback to systematically guide exploration, refining one best prompt at each iteration while integrating it with diverse examples. This approach ensures that feedback meaningfully shapes the optimization process, improving computational efficiency. PromptAgent often requires 3–4 hours for prompt generation, whereas PW completes the process in under 30 minutes, with superior performance demonstrated across datasets.

3. PW’s Key Differentiators:

a. Dual Optimization: PW optimizes both the prompt instruction and in-context examples in tandem, addressing a broader range of challenges than methods like APO and PromptAgent, which focus solely on instruction refinement.

b. Feedback-Guided Exploration: Unlike APO and PromptAgent, PW tightly integrates feedback into the exploration process, enabling more efficient and directed iterations.

c. Efficiency and Versatility: PW significantly reduces computational overhead while maintaining superior performance across a variety of complex datasets, as shown in our evaluations on GSM8k, AQUARAT, and others.

We will incorporate this detailed discussion and corresponding quantitative comparisons into the revised manuscript to clarify PW’s novelty and advantages over existing feedback-based prompt optimization approaches.

Sorry for the late reply. Thank you very much for your explanation. However, I believe that PromptWizard still has some shortcomings in its methodological design, offering limited inspiration for subsequent research on prompt optimization. Nevertheless, I appreciate your efforts. I have adjusted my score and hope you can make corresponding revisions based on the valuable suggestions.

Overclaim of the innovation part. Actually, integrating error feedback for LLMs to refine the prompts has already been proposed in other works, such as APO (https://openreview.net/pdf?id=WRYhaSrThy), PromptAgent (https://arxiv.org/pdf/2310.16427) and PROMST (https://arxiv.org/pdf/2402.08702), etc. The main framework is mostly close to other works such as APO, PROMST, and PromptAgent. However, the paper did not mention these works and compare with them. 

Thank you for this comment. We will provide details of our contributions and also how PW compares to other feedback based approaches like APO and PromptAgent next. We hope this would clarify any concerns on the innovations and its performance compared to existing approaches.

PromptWizard Contributions:

PromptWizard (PW) achieves its superior performance through three core innovations that collectively differentiate it from existing approaches, ensuring both high accuracy and cost-efficiency. These insights address the limitations of prior work and offer a transferable foundation for future advancements:

1. Feedback-Driven Critique-and-Synthesis Mechanism:
   PW introduces a novel feedback loop where an LLM generates, critiques, and refines prompts and examples iteratively. This mechanism balances exploration (through diverse mutations) and exploitation (guided feedback to converge on optimal prompts). Unlike evolutionary approaches such as PromptBreeder and EvoPrompt, which rely on mutation without structured feedback—leading to uncontrolled exploration and higher costs—PW integrates LLM-driven feedback at every stage to guide prompt refinement. This feedback loop ensures edits align with task requirements, reducing unnecessary evaluations and delivering better results with significantly lower computational costs. Compared to feedback-based systems like APO and PromptAgent, which lack diverse initial mutations, PW expands the search space by generating diverse prompts before applying feedback, preventing premature convergence to suboptimal solutions. This balanced and iterative process ensures task-relevant optimization while reducing computational overhead.

2. Sequential Optimization of Instructions and Examples:
   PW uniquely optimizes instructions and examples in tandem through iterative refinement:

a. Prompt Optimization with Examples: The refined prompt undergoes evaluation with selected examples, and feedback informs further improvements.

b. Example Synthesis: Using feedback, PW synthesizes diverse, task-relevant examples that enhance performance.
By jointly optimizing prompts and examples, PW surpasses methods that treat these elements independently, creating cohesive, task-aware prompts.

3. Incorporation of CoT Reasoning:
   PW integrates Chain-of-Thought (CoT) reasoning during optimization. This step-by-step reasoning framework leverages the refined prompts to guide the LLM in solving tasks effectively, further improving task performance and interpretability.

Takeaway

The key insight is that PW combines feedback-driven critique and synthesis, tandem optimization of prompts and examples, and self-generated reasoning steps into an automated, efficient framework. These elements are fundamental to PW's ability to outperform state-of-the-art methods while being significantly more cost-effective.

While certain components may seem familiar, PW innovates by integrating these techniques into a cohesive system that automates and optimizes prompt engineering effectively. This work lays the foundation for future developments in prompt optimization by demonstrating that these techniques can be adapted and extended to address evolving challenges in this field.

PromptWizard Contributions:

PromptWizard (PW) achieves its superior performance through three core innovations that collectively differentiate it from existing approaches, ensuring both high accuracy and cost-efficiency.

1. Feedback-Driven Critique-and-Synthesis Mechanism:
   PW introduces a novel feedback loop where an LLM generates, critiques, and refines prompts and examples iteratively. This mechanism balances exploration (through diverse mutations) and exploitation (guided feedback to converge on optimal prompts). Unlike evolutionary approaches such as PromptBreeder and EvoPrompt, which rely on mutation without structured feedback—leading to uncontrolled exploration and higher costs—PW integrates LLM-driven feedback at every stage to guide prompt refinement. This feedback loop ensures edits align with task requirements, reducing unnecessary evaluations and delivering better results with significantly lower computational costs. Compared to feedback-based systems like APO and PromptAgent, which lack diverse initial mutations, PW expands the search space by generating diverse prompts before applying feedback, preventing premature convergence to suboptimal solutions. This balanced and iterative process ensures task-relevant optimization while reducing computational overhead.

2. Sequential Optimization of Instructions and Examples:
   PW uniquely optimizes instructions and examples in tandem through iterative refinement:

a. Prompt Optimization with Examples: The refined prompt undergoes evaluation with selected examples, and feedback informs further improvements.

b. Example Synthesis: Using feedback, PW synthesizes diverse, task-relevant examples that enhance performance.
By jointly optimizing prompts and examples, PW surpasses methods that treat these elements independently, creating cohesive, task-aware prompts.

3. Incorporation of CoT Reasoning:
   PW integrates Chain-of-Thought (CoT) reasoning during optimization. This step-by-step reasoning framework leverages the refined prompts to guide the LLM in solving tasks effectively, further improving task performance and interpretability.

Takeaway

The key insight is that PW combines feedback-driven critique and synthesis, tandem optimization of prompts and examples, and self-generated reasoning steps into an automated, efficient framework. These elements are fundamental to PW's ability to outperform state-of-the-art methods while being significantly more cost-effective.

While certain components may seem familiar, PW innovates by integrating these techniques into a cohesive system that automates and optimizes prompt engineering effectively. This work lays the foundation for future developments in prompt optimization by demonstrating that these techniques can be adapted and extended to address evolving challenges in this field.

Comparison with SOTA:

PromptWizard (PW) introduces a unique framework that differentiates itself from evolutionary algorithms like PromptBreeder and EvoPrompt, as well as feedback-based systems such as APO and PromptAgent.

Comparison with Evolutionary Algorithms:
PromptBreeder and EvoPrompt rely on evolutionary strategies to generate and refine prompts through random mutation and recombination. While these methods explore diverse prompt variants, they lack a systematic mechanism to evaluate and refine prompts iteratively based on feedback or task-specific performance. In contrast, PW combines feedback-driven optimization with diverse exploration, ensuring that each iteration improves performance by systematically critiquing and refining prompts and examples. This leads to more efficient and targeted optimization compared to the more stochastic nature of evolutionary approaches.

Comparison with Feedback-Based Systems:
Systems like APO and PromptAgent utilize feedback for prompt refinement but are limited in scope. APO focuses on editing and regenerating prompts using feedback gradients, often struggling with complex tasks due to a lack of diverse exploration. PromptAgent employs Monte Carlo Tree Search for prompt refinement, which is computationally expensive and decouples feedback from exploration. PW addresses these limitations by tightly integrating feedback into its iterative refinement process, enabling more guided exploration of both prompts and examples, leading to significant gains in accuracy and efficiency.

Key Novelty and Innovation of PW:
While feedback and iterative prompt refinement are established concepts, PW extends their applicability by innovating in two key areas: (1) efficient and systematic prompt optimization tailored for task-specific performance and (2) joint optimization of prompts and examples, which is critical for tasks requiring nuanced context. These innovations make PW particularly effective for complex, multi-step reasoning tasks, as demonstrated by its superior performance across diverse datasets.

Thank you for your insightful review and positive feedback. We appreciate your suggestions, which will enhance the manuscript. Below are our responses to address your concerns:

My primary concern with this paper is that while the results are impressive, I am struggling to identify the key-insight or transferable idea. What makes PromptWizard better than all the systems it beats? What is the big idea I should take away from this paper?

PromptWizard (PW) achieves its superior performance through three core innovations that collectively differentiate it from existing approaches, ensuring both high accuracy and cost-efficiency. These insights address the limitations of prior work and offer a transferable foundation for future advancements:

1. Feedback-Driven Critique-and-Synthesis Mechanism: PW introduces a novel feedback loop where an LLM generates, critiques, and refines prompts and examples iteratively. This mechanism balances exploration (through diverse mutations) and exploitation (guided feedback to converge on optimal prompts). Unlike evolutionary approaches such as PromptBreeder and EvoPrompt, which rely on mutation without structured feedback—leading to uncontrolled exploration and higher costs—PW integrates LLM-driven feedback at every stage to guide prompt refinement. This feedback loop ensures edits align with task requirements, reducing unnecessary evaluations and delivering better results with significantly lower computational costs. Compared to feedback-based systems like APO and PromptAgent, which lack diverse initial mutations, PW expands the search space by generating diverse prompts before applying feedback, preventing premature convergence to suboptimal solutions. This balanced and iterative process ensures task-relevant optimization while reducing computational overhead.

2. Sequential Optimization of Instructions and Examples: PW uniquely optimizes instructions and examples in tandem through iterative refinement:

a. Prompt Optimization with Examples: The refined prompt undergoes evaluation with selected examples, and feedback informs further improvements.

b. Example Synthesis: Using feedback, PW synthesizes diverse, task-relevant examples that enhance performance. By jointly optimizing prompts and examples, PW surpasses methods that treat these elements independently, creating cohesive, task-aware prompts.

3. Incorporation of CoT Reasoning: PW integrates Chain-of-Thought (CoT) reasoning during optimization. This step-by-step reasoning framework leverages the refined prompt to guide the LLM in solving tasks effectively, further improving task performance and interpretability.

Takeaway

The key insight is that PW combines feedback-driven critique and synthesis, tandem optimization of prompts and examples, and self-generated reasoning steps into an automated, efficient framework. These elements are fundamental to PW's ability to outperform state-of-the-art methods while being significantly more cost-effective. While certain components may seem familiar, PW innovates by integrating these techniques into a cohesive system that automates and optimizes prompt engineering effectively. This work lays the foundation for future developments in prompt optimization by demonstrating that these techniques can be adapted and extended to address evolving challenges in this field.

While the writing was reasonable, the fairly complex pipeline of evolutionary optimizers and sequential optimization made it difficult to understand how the two approaches work together.. How does the sequential optimization interact with the iterative optimization? Is it really just a two stage pipeline?

Thank you for your insightful comment. We acknowledge that the complexity of the pipeline might have caused some confusion, and we will revise our writing to make it clearer. PromptWizard (PW) is indeed a two-stage sequential pipeline where feedback-driven critique-and-synthesis plays a central role in both stages:

1. Stage 1 (Iterative Prompt Optimization): This stage focuses solely on refining the prompt instruction. Using an evolutionary approach, diverse initial prompts are generated and iteratively refined through feedback-driven critique-and-synthesis to converge on an optimized prompt.
2. Stage 2 (Sequential Optimization of Prompt and Examples): In this stage, the refined prompt from Stage 1 is paired with selected examples, and both are optimized together. The critique-and-synthesis mechanism ensures alignment between the examples and the prompt while synthesizing new examples for improved task performance.

The critical feedback loop ensures that both stages iteratively refine their respective components, yet the overall process follows a sequential structure: prompt optimization first (Stage 1), followed by joint optimization of the prompt and examples (Stage 2). We will clarify this interaction further in the revision to ensure accessibility and coherence.

Thank you for your insightful review and positive feedback. Below are our responses to address your concerns:

Although the authors have written about the difference between PW and existing prompt optimization methods. The technical novelty of the proposed method is still somewhat weak. The approach of refining prompt instructions and selecting the most effective in-context examples are widely recognized concepts.

PromptWizard (PW) achieves its superior performance through three core innovations that collectively differentiate it from existing approaches, ensuring both high accuracy and cost-efficiency. These insights address the limitations of prior work and offer a transferable foundation for future advancements:

1. Feedback-Driven Critique-and-Synthesis Mechanism: PW introduces a novel feedback loop where an LLM generates, critiques, and refines prompts and examples iteratively. This mechanism balances exploration (through diverse mutations) and exploitation (guided feedback to converge on optimal prompts). Unlike evolutionary approaches such as PromptBreeder and EvoPrompt, which rely on mutation without structured feedback—leading to uncontrolled exploration and higher costs—PW integrates LLM-driven feedback at every stage to guide prompt refinement. This feedback loop ensures edits align with task requirements, reducing unnecessary evaluations and delivering better results with significantly lower computational costs. Compared to feedback-based systems like APO and PromptAgent, which lack diverse initial mutations, PW expands the search space by generating diverse prompts before applying feedback, preventing premature convergence to suboptimal solutions. This balanced and iterative process ensures task-relevant optimization while reducing computational overhead.

2. Sequential Optimization of Instructions and Examples: PW uniquely optimizes instructions and examples in tandem through iterative refinement:

a. Prompt Optimization with Examples: The refined prompt undergoes evaluation with selected examples, and feedback informs further improvements.

b. Example Synthesis: Using feedback, PW synthesizes diverse, task-relevant examples that enhance performance. By jointly optimizing prompts and examples, PW surpasses methods that treat these elements independently, creating cohesive, task-aware prompts.

3. Incorporation of CoT Reasoning: PW integrates Chain-of-Thought (CoT) reasoning during optimization. This step-by-step reasoning framework leverages the refined prompt to guide the LLM in solving tasks effectively, further improving task performance and interpretability.

Takeaway

The key insight is that PW combines feedback-driven critique and synthesis, tandem optimization of prompts and examples, and self-generated reasoning steps into an automated, efficient framework. These elements are fundamental to PW's ability to outperform state-of-the-art methods while being significantly more cost-effective. While certain components may seem familiar, PW innovates by integrating these techniques into a cohesive system that automates and optimizes prompt engineering effectively. This work lays the foundation for future developments in prompt optimization by demonstrating that these techniques can be adapted and extended to address evolving challenges in this field.

The presentation of the overall pipeline of the approach could be improved. For example, moving the main algorithm in the appendix into the main text will make the presentation clearer and avoid making readers lose among the proposed components.

Thank you for this suggestion. In the revised manuscript, we will move the main algorithm from the appendix to the main text. This adjustment will ensure that readers can follow the proposed components more easily and see how they integrate into the overall pipeline.