Product managers often get stuck when breaking down requirements? Use DiffMind with GPT, Claude, and Gemini to fill in all the gaps in your knowledge.

1. Have you ever gotten stuck on requirement breakdown in this way?

Last week, while helping a knowledge-based paid startup write its PRD, I spent three hours stuck on the requirements document for the "learning path feature":
“From user registration to course completion, three learning paths need to be designed, but simply listing the steps is not enough—I’m always worried that something is missing?”
“"For example, how to recall learning after it is interrupted? How to adapt the difficulty of content to users with different levels? When asking these questions with a single AI, it either only talks about 'basic functions' or goes into 'technical implementation,' and none of them can fully cover the questions."‘

This is a common daily routine for many product managers: writing PRDs feels like walking a tightrope, requiring them to understand user psychology, consider technical implementation, and balance business goals. However, the limitations of focusing solely on AI are too obvious—GPT excels at logical frameworks but leans towards technology, Claude understands user psychology but easily overlooks functional details, and Gemini prioritizes user experience but neglects data accumulation. Ultimately, the result is either rework or a "lucky" launch, only to later discover that the requirements weren't fully understood.

Until I started using DiffMindOnly then did I truly experience the thrill of "multiple models breaking down requirements simultaneously": for the same requirement, three top AIs simultaneously output breakdown results, filling in blind spots from different dimensions, and combined with my own judgment, the completeness of the requirement document directly doubled.

II. DiffMind: A "Prism" for Decomposing Requirements“

Simply put, DiffMind isMulti-model requirement decomposition workbench：
When you submit a requirement, GPT, Claude, and Gemini will simultaneously output breakdown results—including a "logical framework" (how to break it down into steps), "user scenarios" (how different user groups will use it), a "feature list" (which are core functions and which are hidden requirements), and "risk warnings" (potential pitfalls). You don't need to switch platforms; you can see the "thinking reports" from the three AIs on the same interface, and then integrate them to generate the most comprehensive solution for your requirement.

III. Real-world case study: Deconstructing the "clock-in function requirement" using DiffMind“

To illustrate the effect, we will conduct an experiment using a specific requirement:
Prompt"Please break down the requirement to 'add a check-in function to a social app', including: user scenarios, core functionalities, data metrics, and potential risks."“

1. GPT breakdown: Like a "technical document writer," the logic is clear but superficial.

• Logical frameworkThe process is divided into "Check-in Process" (user enters the page → fills in content/selects a theme → submits → displays successful check-in) and "Data Display" (personal check-in calendar, number of consecutive check-in days).
• Core FunctionsCheck-in interface, content theme library, check-in record page
• Data metrics: Number of users checking in, consecutive check-in rate, content interaction (likes/comments)
• Risk WarningTechnical implementation (calendar component adaptation), user experience (avoiding duplicate check-in restrictions):

AdvantagesThe logic is the most logical, making it suitable for writing technical documents;blind spotThe article did not mention "incentive mechanisms for check-ins" (such as rewards for consecutive check-ins) or "user psychological satisfaction" (such as social sharing after check-ins).

2. Claude's breakdown: Like a "user experience analyst," with many details but incomplete functional coverage.

• Logical frameworkIt is divided into "user behavior path" (discovering the check-in entry point → participating in the check-in → viewing feedback → repeat purchase of check-in) and "operational strategy" (check-in rule design, user segmentation).
• Core FunctionsBasic check-in interface, theme templates, check-in dynamic wall, consecutive check-in rewards, check-in data review page.
• Data metrics: Number of users who check in, consecutive check-in rate, sharing rate, user retention rate (retention rate the day after check-in)
• Risk WarningUser privacy (whether check-in content is made public) and operating costs (long-term investment in the reward mechanism).

AdvantagesIt delves most deeply into the user's perspective, considering details such as "dynamic wall" and "data review";blind spotThe article does not mention the "technical implementation difficulty" (such as the loading speed of the dynamic wall) or the "functional priority" (which features are essential for the MVP and which can be iterated later).

3. Gemini's breakdown: Like a "business strategist," focusing on implementation but neglecting data accumulation.

• Logical frameworkIt is divided into "user acquisition" (check-in entry design, sharing rewards) and "user retention" (check-in reminders, consecutive check-in rules).
• Core FunctionsCheck-in entry (homepage/personal page), check-in theme library (daily/weekly topics), check-in sharing (to WeChat Moments/Weibo), check-in calendar, consecutive check-in rewards.
• Data metricsNew user check-in rate, number of new users acquired through sharing, and daily active user growth (check-in days vs. non-check-in days).
• Risk Warning: The user experience when sharing to third-party platforms and the fairness of the reward mechanism.

AdvantagesThe business objectives are most clearly defined, making it suitable for focusing on user acquisition and retention;blind spotThe report did not mention the "deep value of check-in data" (such as keyword analysis of user check-in content, used to optimize course recommendations).

4. DiffMind's Integrative Value: Complementing the "Blind Spots" of Three AI Systems

• Logical levelThe combination of GPT's process design, Claude's user experience, and Gemini's business strategy covers the entire chain from "user → function → business".
• Functional coverageIt includes basic check-in (GPT), user incentives (Claude), customer acquisition (Gemini), data review (Claude), and technical risk assessment (GPT), offering 30% more functionalities than a single AI solution.
• Decision basisThrough comparison, I can clearly see that the MVP stage must include "basic check-in + sharing rewards" (Gemini's user acquisition) and "consecutive check-in days" (Claude's data review), while the "dynamic wall" can be placed in the V2 version (Claude's detailed features).

IV. Three Core Values of Using DiffMind to Decompose Requirements

1. Discovering the "blind spots in thinking about single AI"“

Different models use different training data and optimization directions: GPT comes from massive amounts of code and technical documentation, Claude focuses on law and humanities, and Gemini pays more attention to business and user experience. When you let them break down requirements together, it's like having three people with different professional backgrounds help you "find the mistakes"—you will no longer rely on just one AI's "partial answer," but can cover blind spots in all dimensions such as technology, users, and business.

For example, when breaking down a "learning path", GPT may only list the steps, Claude will remind users of "the psychological reasons why users give up halfway", and Gemini will calculate "whether this path can improve the course completion rate". After completing the requirements document from three perspectives, there will be no more pitfalls such as "users can't use it".

2. Compare the rationality of different logical paths.“

For the same requirement, different AIs will give different "priority rankings": some emphasize "user experience", some emphasize "technology implementation", and some emphasize "business goals". DiffMind allows you to intuitively compare these logical differences and then make decisions based on your project stage (MVP/iteration phase/maturity phase).

For example, when analyzing the pricing of courses on a knowledge-paying app, GPT might suggest "pricing by the lesson," Claude might suggest "tiered pricing based on user willingness to pay," and Gemini might suggest "dynamic pricing based on course completion rate." By comparing these approaches, you'll find that "pricing by the lesson" is the most reliable (simple and direct) approach during the MVP phase, while "tiered pricing based on user willingness to pay" is tested during the iteration phase (to increase ARPU). This demonstrates the reliability of decision-making through "multi-model comparison."

3. Enhance the completeness and persuasiveness of PRD documents.“

The most troublesome part of writing a PRD is "how to convince your boss/team that your requirement design is correct." When you integrate the breakdown results of three AIs using DiffMind, the document will contain:

• Technical side: GPT's logical flow + risk warning ("This module development will take 3 weeks and has 3 potential bugs")
• User side: Claude's user scenarios + experience details ("Users may forget to check in, so 3 reminders need to be set")
• Business side: Gemini's data metrics + ROI analysis ("The check-in feature is expected to increase daily active users by 20%, requiring a budget of 50,000 for initial promotion")

Such documentation makes the boss think, "You've thought this through thoroughly," the developers know "where the pitfalls are," and the operations team understand "how to promote it"—this is the value of documentation brought about by "multi-model assistance."

V. Comparison of Requirements Decomposition Efficiency: Using DiffMind vs. Not Using DiffMind

Summarize

Product managers' requirement breakdown is essentially about "finding a balance between users, technology, and business." DiffMind doesn't ask you to "replace thinking with AI," but rather to stand on the shoulders of three top AI technologies—you remain a decision-maker, but your thinking is more comprehensive, with fewer blind spots, and your implementation is more stable.

The next time you get stuck writing a PRD, try having DiffMind act as your "requirement breakdown partner": throw in "check-in function," "learning path," and "course pricing," let GPT explain the logic, Claude dig into the details, and Gemini calculate the business, and you're responsible for piecing together these three "thought reports," adding your own judgment. You'll find that those "requirement loopholes" that once gave you a headache have actually already been "targeted" by other AIs.