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How 9 PRDs from Different AIs Made Nearly Identical Apps & What Matters

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How planning erased details while turning PRD notes into a working text search tool

What if building the same app nine times yielded almost identical results, no matter how sophisticated the AI behind it was? In this walkthrough, Matt Maher shows how experimenting with eight different AI models to generate Product Requirements Documents (PRDs) led to a surprising discovery: the intelligence of the AI had far less impact on the final product than expected. Instead, the real fantastic option was the planning system—a factor often overlooked in discussions about AI-driven development. This unexpected outcome challenges the assumption that smarter AI automatically leads to better results and raises important questions about how intent and vision are preserved during the development process.

This overview dives into the fascinating details of the experiment, revealing why the quality of the PRD didn’t always translate into a superior build and how intent loss during planning silently undermines the original vision. You’ll uncover why even the most advanced AI models can fall short without robust systems to bridge the gap between ideation and execution. Whether you’re a developer, product manager, or simply curious about AI’s role in app development, this exploration will leave you rethinking what truly drives success in building software. The results may not make sense at first glance, but they offer valuable lessons for anyone navigating the intersection of AI and human creativity.

AI Model Intelligence vs. Planning

TL;DR Key Takeaways :

  • The intelligence of AI models had minimal impact on the quality of final application builds; the planning system and intent preservation were more critical factors for success.
  • Despite using PRDs from eight different AI models, the final builds were nearly identical due to the standardization enforced by the planning system.
  • A significant challenge identified was the loss of intent during the transition from PRD to build, with 20-30% of requirements often omitted silently.
  • Proposed solutions include explicitly documenting the rationale behind each feature in PRDs and iteratively verifying development plans against the original PRD to prevent omissions.
  • The experiment emphasizes the need to prioritize robust planning systems and intent preservation over relying solely on advanced AI models for better development outcomes.

Do Smarter AI Models Lead to Better PRDs?

The experiment aimed to evaluate whether advanced AI models, such as GPT52 Pro, could produce superior PRDs that would result in higher-quality application builds. To maintain consistency, the same app, a word-focused search tool, was built nine times using PRDs generated by eight different AI models. A uniform build system, Claude Code with Opus 45 in planning mode, was employed throughout the process.

The hypothesis was straightforward: smarter AI models should create more detailed and effective PRDs, leading to better builds. However, the results defied expectations. Despite the varying levels of sophistication in the PRDs, the final builds were remarkably similar. This outcome highlighted a critical realization: the planning system played a more significant role in shaping the results than the intelligence of the AI model itself.

Key Findings: Consistency Across Models

The experiment revealed that the final builds were nearly identical, regardless of the AI model used to generate the PRD. While the PRDs differed in detail and complexity, the build system standardized the outcomes. This consistency underscored the importance of the planning system in determining the success of the development process.

The planning system effectively neutralized differences in PRD quality, making sure uniform results. While this might seem advantageous, it also exposed a significant challenge: the loss of intent during the development process. This silent loss often created gaps between the original vision and the final product, raising questions about the effectiveness of current planning practices.

I Built the Same App 9 Times, The Results Made No Sense

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The Challenge: Intent Loss and Missing Requirements

One of the most significant challenges uncovered was the loss of intent, the reasoning behind specific features, during the transition from PRD to build. Even when PRDs were detailed and comprehensive, the planning process often omitted 20-30% of the specified requirements. This silent omission created a disconnect between the original vision and the final product.

For example, a feature designed to enhance user experience might be included in the PRD but excluded during planning due to perceived complexity or lack of clarity. Without preserving the “why” behind the feature, its omission could go unnoticed until the final build failed to meet expectations. This issue highlighted the need for a more robust approach to preserving intent throughout the development process.

Addressing the Problem: Solutions to Preserve Intent

To address these challenges, two key solutions were proposed to ensure that intent is preserved and requirements are fully implemented:

  • Document Intent in PRDs: PRDs should explicitly include the rationale behind each feature. By explaining the “why” behind every requirement, developers and planning systems can better understand the importance of each feature, reducing the likelihood of omissions during the planning and execution stages.
  • Iterative Verification of Plans: Development plans should be iteratively checked against the original PRD to ensure all requirements are accounted for. This process involves systematically comparing the planned features with the original document to identify and address gaps early, preventing issues from cascading through the development process.

These solutions emphasize the importance of maintaining a clear connection between the original idea and its execution. By focusing on preserving intent and verifying completeness at every stage, developers can achieve more accurate and effective outcomes.

Implications: Rethinking AI’s Role in Development

The findings suggest that the sophistication of AI models matters less than the robustness of the planning system. While advanced models can generate detailed PRDs, their value diminishes if the planning system fails to preserve intent or include all requirements. This shifts the focus from the intelligence of AI models to the processes that bridge the gap between an idea and its execution.

For developers, this means prioritizing tools and practices that ensure intent survives through all stages of development. By doing so, you can achieve results that align more closely with your original vision, regardless of the AI model used. The experiment underscores the importance of refining planning systems and adopting practices that safeguard the integrity of the original concept.

Bridging the Gap Between Vision and Execution

This experiment highlights a critical insight: the gap between an idea and its execution often stems from silent losses during handoffs, not the intelligence of the AI model. While advanced models can enhance the development process, their impact is limited without robust planning systems and a focus on preserving intent.

As a developer, your priority should be to safeguard intent and verify completeness at every stage of the process. By addressing these challenges, you can ensure that your final builds reflect your original vision, delivering outcomes that meet expectations and align with the intended purpose. This approach not only improves the quality of the final product but also strengthens the connection between the initial idea and its realization.

Media Credit: Matt Maher

Filed Under: AI, Guides



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