PromptPex: Automatic Test Generation for Language Model Prompts (2503.05070v1)

Abstract: LLMs are being used in many applications and prompts for these models are integrated into software applications as code-like artifacts. These prompts behave much like traditional software in that they take inputs, generate outputs, and perform some specific function. However, prompts differ from traditional code in many ways and require new approaches to ensure that they are robust. For example, unlike traditional software the output of a prompt depends on the AI model that interprets it. Also, while natural language prompts are easy to modify, the impact of updates is harder to predict. New approaches to testing, debugging, and modifying prompts with respect to the model running them are required. To address some of these issues, we developed PromptPex, an LLM-based tool to automatically generate and evaluate unit tests for a given prompt. PromptPex extracts input and output specifications from a prompt and uses them to generate diverse, targeted, and valid unit tests. These tests are instrumental in identifying regressions when a prompt is changed and also serve as a tool to understand how prompts are interpreted by different models. We use PromptPex to generate tests for eight benchmark prompts and evaluate the quality of the generated tests by seeing if they can cause each of four diverse models to produce invalid output. PromptPex consistently creates tests that result in more invalid model outputs than a carefully constructed baseline LLM-based test generator. Furthermore, by extracting concrete specifications from the input prompt, PromptPex allows prompt writers to clearly understand and test specific aspects of their prompts. The source code of PromptPex is available at https://github.com/microsoft/promptpex.

PromptPex: Automatic Test Generation for LLM Prompts

The paper "PromptPex: Automatic Test Generation for LLM Prompts" presents a robust methodology for enhancing the robustness and testing capabilities of LLM prompts, which are increasingly being integrated into software applications in a manner akin to traditional code. Given the distinctive nature of prompts—where output heavily depends on the interpreting AI model—novel approaches are required for testing, debugging, and maintaining these prompts. PromptPex addresses these needs by facilitating automatic test generation to accommodate these nuances.

Prompts, as discussed in the paper, are unique entities within software systems. They receive inputs and generate outputs but rely on natural language processing, making their behavior less predictable compared to conventional coding functions. Their outputs are inherently non-deterministic because they are subjected to the interpretation of underlying AI models which themselves are evolving. The need for rigorous testing is thereby imperative to ensure prompts function correctly across diverse models and versions.

PromptPex leverages LLMs to automatically generate tests by extracting input and output specifications from prompts. These specifications are utilized to create targeted and diversified tests aimed at ensuring compliance with the specified behavior. By identifying disparities in model outputs during testing, PromptPex can highlight potential issues in prompt formulation or model interpretation that may go unnoticed with conventional testing methods.

The key contributions of this tool include:

1. Automated Test Generation: PromptPex systematizes the process of converting prompts into dynamically generated tests, which are then used to assess how well these prompts function across different AI models. This approach is a departure from existing techniques that focus more on manual testing or require extensive human input.
2. Specification Extraction: The system defines a method to derive input and output specifications from a prompt, establishing clear criteria for output rules that an LLM must adhere to. This preemptive action enables the generation of tests that rigorously check the prompt's compliance with its specified rules.
3. Cross-Model Evaluation: By generating a suite of tests that are compatible with multiple LLMs, PromptPex can demonstrate model sensitivity and performance, making it easier to select and transition between models as new architectures become available.

In the evaluation, the authors used PromptPex to generate tests for eight benchmark prompts and assessed the tests with four distinct AI models. The results indicated that PromptPex consistently created more tests that resulted in non-compliant outputs compared to a baseline LLM-based test generator. This suggests that PromptPex can effectively uncover weaknesses and potential failures in prompts, providing developers with valuable insights into both prompt and model behavior.

This research significantly impacts both theoretical and practical aspects of prompt testing and AI model integration:

• Theoretical: It proposes a new framework for understanding how prompts function as code-like entities, offering a structured approach to capture their behavior in testable formats.
• Practical: By automating prompt testing, PromptPex reduces the overhead required for developers to ensure their prompts are robust across models, which is increasingly crucial as companies aim to future-proof their AI-driven applications.

The future of AI development will likely see more sophisticated models, and tools like PromptPex will be vital. These tools can adapt existing prompts to new models, ensuring continuity in application quality and performance. Additionally, expanding support for multilayer prompts and incorporating dynamic context switching could further enhance PromptPex's applicability.

Overall, the PromptPex tool offers significant strides toward bridging the gap in prompt development, testing, and deployment, ensuring AI-driven systems maintain robustness and reliability amidst a rapidly evolving landscape of AI capabilities.