This depends on the tool's privacy settings. When using enterprise or local models (e.g., Ollama with Llama 3), your data and source code are not used to train public models. Always check the provider's privacy policy.

The best approach is to provide up-to-date API documentation in the System Prompt. Models most often hallucinate when they lack knowledge of the latest library updates after their Knowledge Cutoff date.

These tools are excellent at generating repeatable components and mockups based on ready-made libraries (e.g., Tailwind, Shadcn). However, they don't replace a programmer when it comes to performance optimization, advanced state management, and deep backend integration.

Use the Few-Shot Prompting technique. Instead of saying "write tests for this function," give your model one existing test file written using pytest along with its mocked dependencies (unittest.mock), and then ask it to generate more using the same pattern.

Yes, as long as you provide it with the API response structure (e.g., a raw JSON object). LLM models flawlessly generate nested TypeScript interfaces and discriminated unions from the pasted JSON.

Block the tendency of LLM models to use any type. Always start the prompt with the instruction: "Write the component in TypeScript, define strict interfaces (interfaces/types) for props, and avoid type casting."

When generating backend code (e.g., Fast API, Django), models often mix synchronous and asynchronous functions. Explicitly mandate AI: "Use native async/await and the httpx library instead of synchronous requests."