Coding guide
If you are looking for a deepinfra coding plan, start with the coding task you need to complete. This guide shows how to prepare a useful prompt and test the result; it does not assume that a particular plan or feature is available.
Bring a small, reproducible task, the relevant language or framework, and a way to run the answer locally. These starting points help you choose what to inspect next.
You need a function for an existing project and can specify its inputs, outputs, and constraints.
Identify a suitable model before testing the generated function. The deepinfra models guide explains how to compare model choices.
You have API notes and need to find the relevant passage before asking for an implementation.
Retrieve supporting context first. The deepinfra embeddings guide covers the retrieval side of that workflow.
You have a failing test and can share the error, expected behavior, and smallest relevant code sample.
Compare model choices for debugging, then rerun the test yourself. The deepinfra models guide is a starting point for that comparison.
Use a bounded task rather than asking for an entire application. Here is a complete path from request to checked result.
Ask for a Python function that accepts a list of integers and returns the values that occur more than once, in first-seen order. State that an empty list should return an empty list.
Ask for the function, a short explanation, and tests covering an empty input, repeated values, and values that occur only once. Keep the requested output focused on code you can run.
Execute the tests in your own environment. If a case fails, send the failing input, actual output, and expected output in a follow-up prompt. Inspect the revised code before using it elsewhere.
Choose the prompt format that matches the evidence you have. A coding plan search alone does not establish which models, interfaces, or features are currently offered.
New function
Use this format when the task has clear inputs and outputs but no existing code. Name the language, edge cases, and output format so the response can be tested without guessing at the requirements.
Bug fix
Use this format when code already exists. Include the error or incorrect result, the relevant snippet, and what should happen instead. Remove unrelated files and secrets before sharing context.
Code review
Use this format when you want to inspect working code. State whether correctness, security, readability, or performance matters most, and request concrete locations and reproduction steps for each finding.
A useful coding workflow still depends on the quality of the input and independent checks of the output.
A model may choose a language, dependency, or edge-case behavior you did not intend.
What to do instead
State the environment and expected behavior before requesting code.
A plausible answer can contain logic errors or omit cases that matter to your project.
What to do instead
Run tests locally and add cases based on real inputs.
A generated answer cannot establish whether a named coding plan exists or what it currently includes.
What to do instead
Check the provider's current documentation for plan and feature details.
Source snippets may contain credentials, personal information, or proprietary material.
What to do instead
Remove sensitive data and follow your organization's sharing rules.
Write down the inputs, expected outputs, and edge cases, then use them to ask for a focused implementation. Treat the response as a draft until it passes your own checks.
This page cannot confirm a current plan with that name. Check the provider's current documentation for the available offerings and their terms rather than relying on a search phrase.
Whether a particular model suits coding depends on its capabilities and the task. Start with a small, testable request, then inspect the output and run it in your own environment.
Include the language, relevant context, expected behavior, and edge cases. For a bug, add the smallest failing example and state both the actual and expected result.
Do not assume it will. Generated code can contain mistakes, unsuitable dependencies, or incorrect assumptions, so run tests and review it before use.