As of 13 August 2026, AI can explain unfamiliar code to you.

What to actually do

1. Use a tool built for this

   The route this page recommends

2. Do it yourself

   Second choice

   A chat interface, chat-fluent skill, and roughly 30 minutes until you can act on the result.

   How to actually do it

   1. Open the relevant repository in your code editor and identify the file you want explained, its language and framework, and the files it imports.
   2. Gather the surrounding context from the README, package or dependency file, configuration names, entry-point file and relevant tests, removing passwords, API keys, tokens, personal data and confidential business data.
   3. Paste the prompt into ChatGPT, Claude or Gemini and replace each bracketed slot with the repository details and code you are allowed to share.
   4. Ask a separate follow-up for each unresolved dependency or file, pasting only the relevant excerpt and asking the model to distinguish observed facts from inferences.
   5. Compare the explanation with the actual call sites, imports, configuration and tests, then run the existing test or a safe local execution path to check the described inputs, outputs and error handling.
   6. Ask a colleague or technical owner to check any security concern, production side effect or behaviour the model marked as uncertain before relying on the explanation to change code.

   Prompt

   Explain the following unfamiliar code for a working developer or technically confident colleague. Use only the code and context I provide, and label anything you are inferring. Do not invent the purpose of functions, data sources, configuration values or business rules.
   
   First give a short plain-English summary of what the code appears to do. Then explain:
   1. The entry point and the execution flow, in order.
   2. Each important function, class, module or block, including its inputs, outputs and side effects.
   3. External services, databases, files, environment variables and permissions it depends on.
   4. Error handling, unusual cases and likely failure points.
   5. Security or privacy risks that are visible from the supplied code.
   6. Any parts you cannot determine without running the code or seeing other files.
   
   Quote short code fragments when useful, but do not reproduce the whole file. Finish with a list of specific checks I should make in the repository, documentation or tests to confirm your explanation.
   
   Language and framework: [language and framework]
   Purpose or task context: [what this code is believed to support]
   Relevant repository structure: [folders and related filenames]
   Code:
   [paste code here, after removing passwords, API keys, tokens, personal data and confidential business data]
   
   Do not propose changes unless I ask for them.

   Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.

3. Hand it to a person

   The distant third

   A person who owns the outcome does this end to end, worth it when the failure is dear.