AI for coding: how to get reliable help from Llama

All four Llama models in our chat are built to produce code as well as text, according to Meta's model cards, and they make useful programming partners: explaining unfamiliar code, writing small functions, drafting tests, turning an error message into a fix. They also make mistakes with total confidence, so the right attitude is that of a code reviewer: read every suggestion as if a quick but inexperienced colleague had sent it, and merge nothing you have not checked. This guide covers how to ask, how to check, and where the limits are in our chat.

What Llama is good at in everyday coding

• Explaining code. Paste a function you inherited and ask what it does, line by line, and what could break it.
• Small, well-defined pieces. A function that parses a date string, a regular expression, a SQL query, a shell one-liner, a CSS layout.
• Debugging from an error. Paste the full error message together with the code around it.
• Tests. "Write unit tests for this function, including edge cases" often surfaces cases you had not considered.
• Translating between languages. Python to JavaScript, a Bash script to PowerShell.
• Talking through a design. Describe what you plan to build and ask for weak spots before you write a line.

It is weaker at large changes across many files it cannot see, at libraries that changed after its knowledge cut-off, and at anything that depends on the exact setup of your machine.

Give the context a teammate would need

Weak:
my code doesnt work, fix it
[300 lines pasted]

Strong:
Node.js 22, plain JavaScript, no libraries. formatDue(dateString)
should print the due date in the user's local time, e.g. "Mon 3 March".
For users in Brazil it shows the day before.

What I see in the console:
[paste the exact output]

Code (only formatDue and the line that calls it):
[paste]

Tell me the cause first, then give a fixed formatDue. Keep its name
and its single argument, and don't add a date library.

The strong version names the language and versions, states what should happen and what happens instead, includes the exact error, trims the code to what matters and says what form the fix should take. Each of those removes a guess. A checklist for any coding prompt:

1. Language, version, framework and libraries.
2. What the code should do, and what it does instead.
3. The exact error message or wrong output, copied rather than paraphrased.
4. A minimal code sample that still shows the problem.
5. Constraints: "no new dependencies", "must run on Windows", "keep it readable for beginners".

A safe workflow: plan, write, run, review

1. Plan first. For anything bigger than one function, ask for the approach before the code: "Outline how you would structure this, with files and functions, but don't write code yet." Fixing a plan is cheap; fixing 200 lines is not.
2. Work in small steps. One function or one change at a time. Long answers leave more room for mistakes, and answers in our chat have a length limit. If code is cut off, reply "continue from the last complete line".
3. Run it yourself. Llama cannot execute code in our chat. It predicts what working code looks like; only running it tells you whether it works.
4. Feed back real results. Paste the new error or the wrong output. A few short rounds like this usually get further than one long explanation.
5. Review before you commit. Read every line. Then ask "What edge cases does this miss?" and "Is anything here insecure?" as separate follow-ups.

Watch for these failure modes

Invented functions and options

Models sometimes call a method that does not exist or use a command-line flag that belongs to a different tool. If something looks unfamiliar, check the official documentation before you build on it.

Outdated APIs

Training data stops at a cut-off: August 2024 for Llama 4 and December 2023 for Llama 3.3 and 3.1, according to Meta's model cards. Libraries released or changed after that are unknown to the model, and it will often suggest the older way. Tell it which version you use, and paste the relevant part of the current docs when it matters.

Code that works but is unsafe

Look out for SQL built by joining strings, missing input validation, secrets written into source files, disabled certificate checks and overly broad file permissions. Ask explicitly for a security review of anything that faces the internet.

Plausible but wrong logic

Off-by-one errors, time zones, rounding on money, empty lists. Write a test with known answers before you trust the result.

Keep secrets out of the chat

Never paste API keys, passwords, private keys, connection strings or customer data. Replace them with placeholders such as API_KEY_HERE. Your messages travel through our server and a third-party inference provider to generate the answer; treat any secret that has left your machine as exposed and rotate it. If your employer has rules about sharing code with outside services, follow them. More in AI privacy and safety.

Which Llama model for code?

Start with the default, Llama 4 Maverick. Llama 3.3 70B is a solid text-only alternative and a good second opinion on the same problem. Llama 3.1 8B is quick for small jobs such as a regex or a one-line command, but double-check its output on anything subtle. The model guide compares all four.

Want Llama inside your own editor or app? The models are open-weight, so you can run Llama locally or call it through an API. Check the conditions on our Llama license page first.

Prompts to copy

• "Explain this code to a developer who is new to the project. Then list three ways it could fail in production. [code]"
• "Write a [language] function that [does X]. Include type hints, a docstring and three example calls with the expected output."
• "Write unit tests with [framework] for this function. Cover normal input, empty input and invalid input. [code]"
• "Convert this [language A] code to [language B] with the same behaviour. Point out anything that has no direct equivalent."
• "Review this code for security problems only, ranked by severity. [code]"

Use the code assistant for one-off tasks, browse the coding prompts, read Llama for developers, or ask Llama to explain a piece of code right now.

FAQ

Can Llama run my code?

No. In our chat it only reads and writes text. It cannot execute code, open your files or access your repository, so run and test everything yourself.

Can I use code that Llama writes in my project?

Generally yes, but you are responsible for it: review it, test it, and be careful with longer snippets that look like they come from a known project, since those may carry license obligations. This is general information, not legal advice.

Is it better in some languages than others?

Usually, yes. Mainstream languages with a lot of public code (Python, JavaScript, Java, PHP, SQL and the like) tend to get better answers than rare languages or very new frameworks. Whatever the language, test the result.

Why does it suggest an old version of a library?

Its training data ends at a cut-off date. Tell it which version you use and paste the relevant documentation, and it can work from that instead of from memory.

Is it safe to paste my company's code?

Only if your company allows it. Remove secrets and personal data first. When in doubt, ask about a simplified example that shows the same problem.