Prompt Stability Matters: Evaluating and Optimizing Auto-Generated Prompt in General-Purpose Systems
summary
The gist
The gist: This work introduces prompt stability as a key factor for building robust and effective prompt generation systems in general-purpose multi-agent systems, arguing that consistency across
In short
The work introduces prompt stability as a key factor for reliable general-purpose multi-agent systems, arguing consistency across repeated runs is more important than single task success. It proposes semantic stability as a metric to quantify this consistency and develops a system that uses this feedback to iteratively improve both prompt quality and overall performance.
Key concepts
- Prompt Stability
- This refers to how consistently an LLM produces the same kind of output when given the same prompt multiple times. It measures the reliability of a prompt by checking if its responses remain semantically similar across different executions, which is crucial for building dependable AI systems.
- Semantic Stability S(p)
- This metric quantifies prompt stability by calculating the average pairwise cosine similarity between different outputs generated from the same prompt. A higher score indicates that the outputs are more semantically consistent, meaning the prompt reliably guides the model to produce similar results.
- Stability-Guided Optimization Framework
- This is a system that uses stability feedback to improve prompts and planning simultaneously. If a prompt's stability is low, an agent diagnoses the instability and revises parts of it. This iterative process aims to make prompts more consistent while also aligning the planned steps better with the desired final outcome.
- Planner Alignment Objective
- This objective focuses on minimizing the gap between what the system plans to do (the planner intent) and what actually happens during execution. By adjusting subtask boundaries and injecting corrective signals, this mechanism ensures that the agent's actions stay close to the original high-level goal.
Terminology used across episodes
This episode discusses
- Prompt Stability Matters: Evaluating and Optimizing Auto-Generated Prompt in General-Purpose Systems · Paper Radio
- LLM-AutoDiff: Auto-Differentiate Any LLM Workflow
- Large Language Models Are Human-Level Prompt Engineers
- Efficient multi-prompt evaluation of LLMs
- CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
- Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference
- Cross-Task Generalization via Natural Language Crowdsourcing Instructions
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
- The Power of Scale for Parameter-Efficient Prompt Tuning
- Prefix-Tuning: Optimizing Continuous Prompts for Generation
- RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning
- Non-Determinism of "Deterministic" LLM Settings
- Are Large Language Models Consistent over Value-laden Questions?
- Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
- Data Interpreter: An LLM Agent For Data Science
- AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
- Self-Evolving Multi-Agent Collaboration Networks for Software Development
- Evaluating Large Language Models Trained on Code
- DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models
- Measuring Mathematical Problem Solving With the MATH Dataset
- InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks
The paper
Prompt Stability Matters: Evaluating and Optimizing Auto-Generated Prompt in General-Purpose Systems · Read on arXiv
Ke Chen, Yufei Zhou, Xitong Zhang, Haohan Wang
School of Information Sciences, University of Illinois Urbana-Champaign · Department of Economics, Duke University
Automatic prompt generation plays a crucial role in enabling general-purpose multi-agent systems to perform diverse tasks autonomously. Existing methods typically evaluate prompts based on their immediate task performance, overlooking the intrinsic qualities that determine their reliability. This outcome-centric view not only limits interpretability but also fails to account for the inherent stochasticity of large language models (LLMs). In this work, we bring attention to prompt stability-the consistency of model responses across repeated executions-as a key factor for building robust and effective prompt generation systems. To quantify this, we propose semantic stability as a criterion for assessing the response consistency of prompts. Based on the proposed metric, we developed the first stability-aware general-purpose prompt generation system that leverages stability feedback to iteratively enhance both prompt quality and system-level performance. Furthermore, we establish a logical chain between prompt stability and task success by analyzing the structural dependencies within our system, proving stability as a necessary condition for effective system-level execution. Empirical results across general and domain-specific tasks demonstrate that our stability-aware framework improves both accuracy and output consistency. By shifting the focus from one-off results to persistent reliability, our work offers a new perspective on prompt design and contributes practical tools for building more trustworthy general-purpose systems. The UI and source code for our system are publicly available at: https://xucheng63.github.io/MyDataPilot/.
Transcript
Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.
Tom: Today's paper: "Prompt Stability Matters".
Jane: The gist: This work introduces prompt stability as a key factor for building robust and effective prompt generation systems in general-purpose multi-agent systems,
Tom: First, who's behind it and why it matters.
Paper summary: Tom: So, looking at the whole paper, Prompt Stability Matters: Evaluating and Optimizing Auto-Generated Prompt in General-Purpose Systems, the authors really push for a fundamental change in how we think about prompt evaluation.
Jane: They’re arguing that outcome-centric evaluation is too narrow because it ignores the inherent randomness of large language models <ref:2505.13546#pg2>.
Lu: Their main contribution is proposing semantic stability as the metric to capture response consistency across repeated executions, and then building a system, Promptor, that uses this feedback to iteratively improve both prompt quality and system performance <ref:2505.13546#pg3>.
Meng: It seems like they've moved beyond just writing prompts by creating a whole loop where the AI checks its own stability and fixes itself based on that measured consistency <ref:2505.13546#pg3>.
Tom: And they successfully showed that this stability metric isn't just a theoretical concept; it has a measurable, positive impact on task success rates across general and domain-specific tasks <ref:2505.13546#pg2>.
Jane: The implication for the world is that we can start designing general-purpose multi-agent systems with this stability feedback built in from the start, focusing on long-term reliability instead of just hoping for a good first try <ref:2505.13546#pg1>.
Conclusion: Tom: So, we just finished looking at this paper, "Prompt Stability Matters." Basically, they're saying that when you're building big AI systems that do lots of different jobs—like a multi-agent system—you shouldn't just care if the first answer is right.
Jane: Right. They argue that consistency over time is way more important than getting one perfect result right now. So, they introduce this idea called semantic stability, which measures how much the AI's answers stay semantically similar when you run the same prompt again.
Lu: From a representation learning side, I found their theoretical analysis interesting. They show that the error between what you plan and what actually happens is really just driven by the variance in how those executors respond. That makes sense mathematically.
Meng: But if we're talking about shipping something to production, how do you actually measure that stability without running millions of tests? I need a practical way to know if my prompt is going to break at scale.
Lalam: From my perspective, the authors fine-tuned a LLaMA evaluator specifically for this task. It gives us an automated way to score the consistency of prompts across different tasks, which is useful for iterative refinement. I'm pretty sure the stability metric they used shows a strong positive correlation with actual task success rates.
Tom: A correlation is good, but let's look at what that means for us building general-purpose agents. If we can quantify stability, then we can build systems that don't just guess; they know which prompts are actually reliable before we spend hours training them.
Jane: Exactly. The paper shows a logical chain here where prompt stability isn't just some nice idea; it’s a necessary condition for the whole system to work effectively at the task level. It shifts the focus from hoping for a good one-off answer to building something that is reliably consistent over time.
Lu: That shift is crucial because it gives us an interpretable way to tune these systems, instead of just tweaking parameters blindly. It’s about designing for persistence rather than just immediate output accuracy.
Meng: For deployment, that means we spend less time debugging cascading failures between agents and more time ensuring the core instructions—the prompts—are stable enough to handle the inherent randomness of the LLMs. That's a real cost saver on our end.
Tom: So, if you take away everything in this paper, you can just say they’ve given us a better lens for designing prompts that actually work in complex multi-agent scenarios. It’s about moving from one-off results to persistent reliability.
Jane: It sets up the next thing we need to consider: how do we actually implement this stability feedback loop into a real system? We’ll be looking at that next.
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