​
Introduction

Revolutionize your coding workflow with an intelligent code generator that supports 16 programming languages and GPT models from OpenAI. AI Code helps you prototype functions, generate utility modules, or scaffold features quickly. The tool is designed for both experienced developers and learners, providing readable, reusable code you can review, test, and integrate into your projects.
Always review and test generated code before using it in production. AI output may contain errors, security issues, or incomplete edge case handling.

​
How to Use AI Code

To get started, go to the AI Writing in the left panel and click on the AI Code tab. Follow these steps to generate code with AI Code.
1

Select Programming Language

Choose the target language from the dropdown. The tool supports 16 languages, for example:
  • C, C#, C++
  • Java, PHP, Python
  • Ruby, Perl, Shell
  • JavaScript, TypeScript
  • Go, Swift, Rust
2

Provide Instructions

Enter a clear description of the function or code you want. Include:
  • Purpose and expected behavior
  • Input parameters and types
  • Return values or output format
  • Any library or framework requirements
  • Constraints such as performance, memory, or compatibility
Example instruction: Create a Python function merge_sorted_lists(a, b) that merges two sorted lists into one sorted list. Include unit tests using pytest and handle empty lists.
3

Choose AI Model

Select the GPT model available under your subscription. Higher-capacity models typically produce more accurate and idiomatic code.
4

Click Generate Code

Press the Generate Code button to run the model. Processing time depends on model choice and prompt complexity.Klyra AI Code Web
5

Review, Save, and Export

  • The generated code appears in the right panel.
  • Above the generated code you will find buttons to:
    • Name the code or give the document a title
    • Save the document to All Codes in the AI Panel
    • Download the file to your device
    • Copy the code to clipboard
Use the Name field to keep your repository organized before saving.

​
💡 Tips

  • Provide example input and expected output to improve accuracy.
  • Specify the coding style you prefer, for example PEP8 for Python or Google style for Java.
  • Ask for unit tests and docstrings to make generated code easier to verify.
  • Keep prompts focused and concise for predictable results.

​
📌 Example Use Cases

  • Function Generation: Quickly create reusable functions or scripts based on natural language instructions.
  • Code Acceleration: Speed up development by generating boilerplate code for APIs, classes, or modules.
  • Learning Aid: Beginners can learn syntax and best practices across 16+ programming languages by studying generated examples.
  • Debugging Support: Ask AI Code to reframe or rewrite problematic logic in a cleaner way.
  • Cross-Language Conversion: Transform snippets from one language (e.g., Python) into another (e.g., Java or C#).
  • Prototyping: Generate working prototypes quickly, then refine them manually for production readiness.
Provide clear instructions like “Write a function in Python to calculate factorial using recursion” for the most accurate results.

​
Troubleshooting

  • If output is incorrect or incomplete, refine the instruction with more constraints or examples and regenerate.
  • For runtime errors, run the generated code locally and request fixes by providing the error trace in a follow-up instruction.
  • If the model produces a non-preferred library, explicitly list the libraries you want or do not want.

​
Security and Compliance

  • Do not include secrets, API keys, or private credentials in your instructions or prompts.
  • Review license and attribution requirements for any third-party libraries suggested in the generated code.