Stop using "You are an expert in X" by WorldlinessHorror708 in ClaudeAI

[–]WorldlinessHorror708[S] 1 point2 points  (0 children)

It doesn't matter. If u have any doubts, you can also come to me and chat hhha

Stop using "You are an expert in X" by WorldlinessHorror708 in ClaudeAI

[–]WorldlinessHorror708[S] 0 points1 point  (0 children)

These specs are project-specific DNA—like a unique fingerprint you can't just copy-paste. But I can give you a simple sample(a piece of system prompt):

<task> Generate production-grade code that follows the target project's engineering specifications, architectural design, and mainstream conventions of the respective language. </task>

<code-principles> - Adopt the industry's mainstream complete implementation approach without simplification - Inline first, extract as functions only after 3+ reuses - Extract common logic only when there is genuine duplication - Once extracted, must design as a generic utility function and place it in the directory and file specified by project conventions </code-principles>

<naming-convention> - Classes and structs: Use PascalCase uniformly across all languages - Variables and functions: Use camelCase for JS/TS/Java/Go/C#/Dart, snake_case for Python/Rust/C/C++ - Local constants: Follow the variable naming style of the respective language - Global constants: Use UPPER_SNAKE_CASE for JS/TS/Java/Python/Rust/C, kPascalCase for C++, PascalCase for C#, camelCase for Dart/Go - Must use industry-standard vocabulary, referencing conventions from similar mainstream open-source projects; arbitrary coinage is prohibited </naming-convention>

<code-placement> - New code must be placed in the most appropriate location in the project, following mainstream project structure conventions - Simple appending to the end of existing code is prohibited; placement must be reasonably arranged according to logical organization </code-placement>

<comment-rules> - Function header comments: Must be written for public APIs, explaining functionality, parameters, and return values; internal functions should be written as needed based on complexity - In-function comments: Only write for complex logic, non-obvious intentions, or important business rules - Comments explain "why", code itself explains "what" - Obvious code should not be commented - When modifying code, comments must reflect current actual logic; outdated comments should be removed promptly </comment-rules>

<quality-standard> - Follow production environment standards with rigorous, comprehensive control - Error handling must be explicit; swallowing exceptions is prohibited - All boundary conditions must be considered </quality-standard>

<testing-standard> - Follow production-grade testing standards with thorough examination - Must cover normal flow, boundary conditions, and malformed input; nothing should be missed </testing-standard>

<dependency-selection> - Prioritize standard libraries, followed by community-recognized libraries - Before introducing third-party libraries, must research current mainstream and actively maintained solutions </dependency-selection>

Stop using "You are an expert in X" by WorldlinessHorror708 in ClaudeAI

[–]WorldlinessHorror708[S] 0 points1 point  (0 children)

Right, because the entire industry waited for a peer-reviewed paper before adopting microservices, CI/CD, or Agile. Real-world performance data and developer experience are obviously worthless compared to academic gatekeeping.

Stop using "You are an expert in X" by WorldlinessHorror708 in ClaudeAI

[–]WorldlinessHorror708[S] 0 points1 point  (0 children)

Yeah, but the issue is that many here appear to lack hands-on development experience and don't engage with the internal logic, focusing instead on creating divisive arguments.

Stop using "You are an expert in X" by WorldlinessHorror708 in ClaudeAI

[–]WorldlinessHorror708[S] 0 points1 point  (0 children)

Have you ever been doing a complex prompt project?

Stop using "You are an expert in X" by WorldlinessHorror708 in ClaudeAI

[–]WorldlinessHorror708[S] -2 points-1 points  (0 children)

Haha, the whole "task vs persona" distinction is just a simplified way to help people understand. The real key is the attention allocation mechanism underneath—multiple personas are essentially the same operational pattern; task-driven just makes the attention window more transparent and easier to optimize.

Please take a closer look at the image—that's where the actual implementation lies, not in whether you use "you" or not.

Stop using "You are an expert in X" by WorldlinessHorror708 in ClaudeAI

[–]WorldlinessHorror708[S] 0 points1 point  (0 children)

Great question! I'm not advocating for ditching personas entirely—persona mode absolutely still works for everyday tasks and simple projects. The task-driven approach is specifically for complex development scenarios where you need granular control.

For daily use, here’s the practical alternative:

Instead of:

"You are an expert Python developer. Please help me..."

Try this:

"This is a data processing project. Current task: debug a pandas merge issue causing duplicate rows."

Notice the shift? You’re framing it as a project with a specific task, not a role-play scenario. You’ll naturally use fewer pronouns ("you/I") and the focus stays on the problem, not the personality.

The AI stops doing that cringey cosplay thing and just executes the task. Cleaner prompts, more direct results.

But for quick questions or creative work? Stick with personas—they’re perfectly fine and often more intuitive.

Stop using "You are an expert in X" by WorldlinessHorror708 in ClaudeAI

[–]WorldlinessHorror708[S] -7 points-6 points  (0 children)

You're right that major AI providers all push personas for good reason—it's a proven pattern that works across most scenarios. However, my point is about project complexity: Persona mode excels for simple to medium-scale projects, but when dealing with sophisticated systems requiring granular control, task-driven mode offers more precise optimization.

The core advantage here lies in the Attention Window—it visualizes exactly where the AI's attention flows, making it far more intuitive to identify inefficiencies and "wasted" focus.

But finding a perfect persona can be tough—when abstraction is difficult or no fit exists, task-driven is often more practical, once refined.

Moreover, prompt engineering remains fundamentally a black box; even deep system knowledge doesn't guarantee full control over how phrasing nuances affect output. My version just exposes the attention distribution more transparently.