Llama AI for developers

For a developer, a chat model is most useful as a fast second pair of eyes: it reads unfamiliar code, suggests tests you forgot, drafts the boring parts and explains an error message in plain words. This page shows seven practical ways to use the Llama models in our chat for that work, each with a prompt you can open and adjust. If you would rather build your own app on Llama, we also explain the Llama API options and how to run Llama on your own machine.

Understand and fix code

The model works only with what you put in the message, so include the relevant lines, the exact error text and the versions you run.

Get an error explained

A stack trace usually points to the symptom, not the cause. Give the model the full message, the lines around it and what you expected, and ask for likely causes ranked from most to least probable.

I get this error in my [language and framework, with versions] project: [paste error and stack trace]. Here is the code where it happens: [paste code]. Rank the likely causes from most to least probable, explain how I can confirm each one, and show the smallest fix for the most likely cause.

Debug it in the chat. For a single snippet, the Code Assistant page is quicker.

Read code you didn't write

Inherited modules and old scripts are faster to learn with a guided tour. Ask for the purpose first, then the flow, then the risky parts.

Explain what this [language] code does. Start with a two-sentence summary, then walk through the main steps in order, and finish with anything that looks fragile, surprising or likely to break with unusual input: [paste code]

Get the walkthrough.

Describe a UI bug from a screenshot

Llama 4 Maverick and Llama 4 Scout can read images, so you can attach a screenshot of a broken layout next to the relevant CSS or markup. The model sees only the picture, not your running page, so add what you expected.

I've attached a screenshot of a layout problem on [page or component]. Expected: [what it should look like]. Here is the relevant markup and CSS: [paste code]. Tell me what is most likely causing the difference and suggest a fix.

Open this prompt, attach your screenshot, and keep Llama 4 Maverick selected.

Write, test and document

Use the model for drafts you review, never for code you merge unread.

Generate tests with edge cases

Models are good at listing cases a tired human skips: empty input, huge input, odd characters, time zones. Ask for the list first so you can see what is covered.

Write unit tests for this [language] function using [test framework]. First list the cases you will cover, including edge cases and invalid input, then write the tests. Don't change the function itself: [paste function]

Write the tests.

Review a change before you push

A quick review catches off-by-one mistakes, missing error handling and unsafe input use. Ask for findings sorted by severity, with the line each refers to.

Review this diff as a careful senior developer. Report bugs, security problems and missing error handling first, then readability issues, each with the line it refers to and a suggested fix. Say clearly if you find nothing serious: [paste diff]

Run the review.

Build a regex or SQL query you can trust

Ask for the pattern together with an explanation and examples that should and should not match, then test it yourself.

Write a [regex flavour or SQL dialect] [regular expression or query] that [what it must do]. Explain each part, then give me a short table of sample inputs with the result I should expect for each, including tricky cases.

Try this prompt.

Draft a README or commit message

Documentation is easier to fix than to start. Paste your notes or the diff and say who will read it.

Write a README for my [type of project] from the notes below. Sections: what it does, requirements, installation, usage with one example, configuration and known limits. Keep it short and use code blocks for commands: [paste notes]

Draft the README.

What to avoid

• Leaking credentials. Strip keys, passwords, connection strings and any customer records from a snippet first. Your messages pass through our server and an inference provider, so follow your employer's rules about sharing code with outside services.
• Trusting library details. The models have a knowledge cut-off and cannot look up current documentation, so newer APIs or options may be missing or invented. Check the official docs.
• Merging untested output. Generated code can compile and still be wrong. Run it, read it and test it like any other contribution.
• Very long files. The chat trims very long messages, so send the relevant function, not the whole repository.

Related pages

• The coding prompt collection and the guide to coding with AI.
• Which model to pick: choosing the right Llama model; Llama 3.1 8B answers fastest.
• Building on Llama yourself: the Llama license in plain words.