I've spent countless hours in my career writing and reviewing Product Requirements Documents (PRDs). They are the bedrock of traditional software development—the detailed maps that are supposed to guide us from an idea to a finished feature. We debate every user story, define every edge case, and get sign-off from every stakeholder. It's a process built on the assumption that meticulous planning prevents poor execution.
But what if the execution part suddenly became... instantaneous?
We're standing at the edge of a new era, one where LLM agents can take a well-defined prompt and generate not just code, but entire features, in a fraction of the time it used to take. This isn't science fiction anymore. For many tasks, the bottleneck in development is no longer the coding; it's the specification. We're now facing the "PRD Paradox": the curious situation where writing the instructions takes longer than it takes an AI to follow them.
The Old Certainty: The Value of the PRD
Traditionally, the PRD has been our single source of truth. Its purpose is noble: to ensure everyone from engineering to marketing is aligned on what we're building, why we're building it, and for whom. A good PRD is a work of art, a testament to a product manager's diligence. It represents hours of meetings, stakeholder alignment, user research, and careful documentation. This effort is justified as a way to de-risk development. Time spent upfront is supposed to save much more time down the line by avoiding ambiguity and rework.
The New Reality: The Rise of the LLM Agent
Enter the LLM agent. These agents aren't just autocomplete on steroids. They are becoming capable of reasoning, planning, and executing complex software development tasks. Give a properly equipped agent a clear objective—"Add a dark mode toggle to the user settings page that respects the user's OS preference as the default"—and it can:
- Identify the relevant files in the codebase.
- Write the necessary HTML, CSS, and JavaScript/React/etc.
- Add a state management hook.
- Even create a unit test to verify the functionality.
The entire process can take minutes, not days. And this is where the paradox hits. The effort to write a traditional, multi-page PRD for that dark mode feature, with formal user stories, acceptance criteria, and stakeholder sign-offs, could easily take a product manager and a team longer than it takes the agent to build the feature from a single, well-crafted prompt.
Is the PRD Dead?
No, but it is evolving. To say the PRD is dead is to misunderstand the problem. The need for requirements and clear goals hasn't vanished. In fact, it's more critical than ever. An LLM agent is a powerful tool, but it's not a mind reader. It will build exactly what you tell it to. If your instructions are vague or your goal is ill-conceived, it will just build the wrong thing faster than ever before.
The spirit of the PRD—strategic alignment, a clear definition of success, and a deep understanding of the user need—remains indispensable. What's becoming obsolete is the format: the long, prose-heavy document that attempts to specify every detail of the "how."
The PRD of the future might look less like a document and more like a collection of high-level goals, user personas, and a suite of expertly crafted prompts. The product manager's role will shift from being a meticulous spec-writer to a master of clarification and a skilled prompter, capable of translating a strategic vision into a precise set of instructions for an AI to execute. The value is in defining the "what" and the "why," not the granular "how."
A New Workflow for a New Era
We need to adapt our processes to this new reality. For self-contained, well-understood features, the workflow might become:
- Goal Definition (The "Mini-PRD"): A product manager or tech lead writes a one-paragraph description of the feature, the user problem it solves, and the acceptance criteria.
- Prompt Crafting: This description is translated into a detailed prompt for the LLM agent.
- Execution & Review: The agent executes the task. A human developer reviews the generated code, tests it, and integrates it.
This is a more agile, iterative process that leverages the speed of AI without sacrificing the strategic thinking that leads to a great product. The PRD isn't gone; it has just become leaner, faster, and more integrated into the development loop itself.
How are you adapting your planning process in the age of AI? Is the PRD still a central part of your workflow, or are you finding new, faster ways to move from idea to execution?
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