AI Python code generator

What people mean by AI code Python

AI code Python, AI coder Python, and Python AI code are the same request: you want a model to write Python for a job you describe. Kimi can do that in chat, in Kimi Code, or through the API model ids such as kimi-k2.7-code. The generator is not a compiler. It predicts a plausible script. You still run it, read it, and keep the tests.

Use chat or Kimi Code when you are the one at the keyboard. Use the API when a product you operate will call the model on its own. Membership pricing and per-token API pricing are different bills. The model family behind the names is on Kimi K2.

A prompt that gets a reviewable script

State the input, the output, the Python version, and what must not happen. Example you can paste: 'Python 3.12. Read a CSV from a path given on the command line. Sum the column named amount. Print one number. If the column is missing, exit with a message on stderr and status 1. Use the standard library only. Include a pytest test with a tiny CSV string.' That prompt names the interface. 'Write a data tool' does not.

Paste the error, not a screenshot of the error, when the script fails. Say which lines you already changed. Ask for a diff or for the whole file, and pick one. Do not paste API keys, customer rows, or private URLs into the chat. A Kimi Work session that can see a repo is convenient and it can also read files you did not mean to share. Point it at one folder.

After the code appears

Create a virtual environment, install only the packages the script imports, and run the test. Python's venv documentation is the reference for the environment. If the model added a dependency you did not ask for, delete it or justify it. Generated code often catches the wrong exception, uses a float for money, or shells out to a command built from a string. Those are the lines to read first.

For a one-off sum of a column, the standard library csv module is enough. You do not need a hosted model in production to add numbers. The model is for the draft. The script that stays in your project should be something you can explain without opening the chat again.

Steps

1. Write the interface. Inputs, outputs, Python version, libraries you allow, and the failure behavior. One paragraph is enough if it is concrete.
2. Ask for tests in the same prompt. A pytest function with a small fixture catches a script that only works on the model's imaginary file.
3. Run it locally. venv, then pytest, then the command on a file you created. Fix from the traceback. Do not paste secrets into the next prompt.
4. Keep the script, drop the transcript. Commit the code and the test. The chat is not version control.