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All Tutorials /ChatGPT

How to Create a Code Explainer GPT

Updated on:
May 12, 2026
By:
Madhav Bhandari
Use this interactive demo to learn how to build and publish a custom code explainer GPT on ChatGPT.

Quick summary

A custom GPT lets you configure a code explainer with tailored instructions, conversation starters, and Code Interpreter capabilities — all without writing a single line of code. This walkthrough shows how to create, configure, and publish your own code explainer GPT directly inside ChatGPT.


Steps

  1. On ChatGPT's main interface, go to the left-side panel and click Explore GPTs.
  2. Click Create at the top-right corner to get started.
  3. Navigate to the Configure tab to begin setting up your GPT.
  4. Set a distinctive display photo by clicking the Add icon and selecting your preferred image file.
  5. Enter a unique Name and Description for your code explainer GPT in their respective input fields.
  6. Write detailed Instructions that define the purpose, behavior, and capabilities of your GPT and type them into the Instructions field.
  7. Add helpful Conversation Starters to guide users on their first interaction with the GPT.
  8. In the Knowledge section, click Upload Files to import any reference documents you want the GPT to use.
  9. In the Capabilities section, enable Code Interpreter & Data Analysis and add any Actions for third-party tool integration.
  10. Go to the Preview Area, type a prompt, and click Submit to test your GPT for the first time.
  11. Review the GPT's performance, then click the Create button at the top-right corner when satisfied.
  12. Choose your preferred access configuration and click Save to proceed.
  13. Your code explainer GPT is now created and published and ready for use.

📌 Why this matters

Building a custom code explainer GPT on ChatGPT allows developers, educators, and technical teams to create a purpose-built AI assistant that can break down complex code in plain language on demand. By configuring tailored instructions, enabling the Code Interpreter, and uploading reference documents, teams can deliver consistent, accurate code explanations without relying on manual effort. Custom GPTs can be shared internally or publicly, making them a scalable solution for onboarding, code review support, and developer education. This capability dramatically reduces the time engineers spend explaining code to non-technical stakeholders or junior team members.
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