𝗠𝘆 𝗔𝗜 𝗔𝗣𝗜 𝗞𝗲𝗽𝘁 𝗙𝗮𝗶𝗹𝗶𝗻𝗴 𝗨𝗻𝘁𝗶𝗹 𝗜 𝗕𝘂𝗶𝗹𝘁 𝗧𝗵𝗶𝘀 𝗖𝗹𝗶𝗲𝗻𝘁

𝗠𝘆 𝗔𝗜 𝗔𝗣𝗜 𝗞𝗲𝗽𝘁 𝗙𝗮𝗶𝗹𝗶𝗻𝗴 𝗨𝗻𝘁𝗶𝗹 𝗜 𝗕𝘂𝗶𝗹𝘁 𝗧𝗵𝗶𝘀 𝗖𝗹𝗶𝗲𝗻𝘁

I built a content summarizer for a side project. I hit rate limits and 503 errors quickly. The SDK hid the logic. I had no control over retry logic. I had to rewrite code when I changed providers.

AI APIs are not like local databases. They throttle requests. They time out. They crash. My code became messy with decorators and hardcoded URLs.

I switched to a generic client. It uses a config object. It uses exponential backoff and jitter. I use requests and json. I treat the AI API as a simple HTTP endpoint.

This approach works for text generation. Follow these steps for your project:

• Use environment variables for config.
• Add logging from day one.
• Separate retry logic from request functions.
• Mock the API during development.

You do not need a large SDK. A small HTTP client handles 90% of cases. It is easier to debug. It is portable. It forces you to learn the protocol.

How do you handle API reliability? Do you use custom clients or SDKs?

Source: https://dev.to/__c1b9e06dc90a7e0a676b/my-ai-api-kept-failing-until-i-built-this-simple-client-13k0