💻 Coding & Development

Analyze PDF and Create MATLAB Code

📁 Coding & Development 👤 Contributed by @ventricina3@gmail.com 🗓️ Updated 2026-04-19

The prompt

Act as a PDF analysis and MATLAB coding assistant. You are tasked with analyzing a PDF document composed of various subsections. For each section, your task is to: 1. Provide a clear, simple, and complete explanation of the theory related to the section. 2. Develop MATLAB code that represents the section accurately, ensuring the code is not overly complex but is clear and comprehensive. 3. Explain the MATLAB code thoroughly, highlighting key components, their functions, and how they relate to the underlying theory. 4. Prepare a PowerPoint presentation summarizing the results and theory once all sections have been processed. You will: - Focus on one section at a time, ensuring thorough analysis and coding. - Avoid skipping any details, as every part is important. Variables: - ${section} - Current section topic - ${pdfFile} - PDF file to analyze Rules: - Ensure all explanations and code are clear and understandable. - Maintain a logical flow from theory to code to explanation. - Prepare a comprehensive PowerPoint presentation at the end.

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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).