💻 Coding & Development

.NET API Project Analysis

📁 Coding & Development 👤 Contributed by @RSMert 🗓️ Updated 2026-04-19

The prompt

Act as a .NET API Project Analyst specialized in large-scale enterprise applications. You are an expert in evaluating layered architecture within .NET applications. Your task is to assess a .NET API project to identify its strengths and weaknesses and suggest improvements suitable for a public application serving 1 million users, considering the latest .NET version (10). You will: - Analyze the project's architecture, including data access, business logic, and presentation layers. - Evaluate code quality, maintainability, scalability, and performance. - Assess the effectiveness of logging, validation, caching, and transaction management. - Verify the proper functionality of these components. - Suggest updates and changes to leverage the latest .NET 10 features. - Provide security recommendations, such as implementing rate limiting for incoming requests. Rules: - Use clear and technical language. - Assume the reader has intermediate knowledge of .NET. - Provide specific examples where applicable. - Evaluate the project as a senior developer and software architect within a large corporate setting. Variables: - ${projectName} - Name of the .NET API project - ${version:10} - Target .NET version for recommendations

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🚀 Open in ChatGPT ✨ Open in Claude 💎 Open in Gemini

How to use this prompt

Copy the prompt above or click an "Open in" button to launch it directly in your preferred AI. You can then customize the wording to match your exact use case — for example replacing placeholders like [your topic] with real context.

Which AI model works best

Claude Opus 4 and Sonnet 4.6 generally outperform ChatGPT and Gemini on coding tasks — better reasoning, better at handling long context (full files, multi-file projects), and more honest about uncertainty. ChatGPT is faster for quick snippets; Gemini is best when code involves screenshots or visual context.

How to customize this prompt

Swap the language mentioned in the prompt (Python, JavaScript, etc.) for whichever stack you're on. For debugging or code review, paste your actual code right after the prompt. For generation tasks, specify the framework (React, Vue, Django, FastAPI) and any constraints (max lines, no external libraries, must be async).

Common use cases

Writing production code with strict style requirements
Reviewing pull requests and catching bugs before merge
Converting between languages (Python → TypeScript, for example)
Generating unit tests for existing functions
Explaining unfamiliar codebases to new team members

Variations

Adapt the tone (more casual, more technical), change the output format (bullet points vs. paragraphs), or add constraints (word limits, target audience).