๐๐๐ถ๐น๐ฑ๐ถ๐ป๐ด ๐ฎ ๐๐น๐ฒ๐ฎ๐ป ๐๐ ๐๐ป๐๐ฒ๐ด๐ฟ๐ฎ๐๐ถ๐ผ๐ป ๐๐ฎ๐๐ฒ๐ฟ
I used three AI providers. OpenAI, Claude, and a local model. My code was a mess. API keys and error handling were everywhere. It was fragile.
I tried multi-provider libraries. They felt too complex. Debugging took too long. I tried a single function. It became a 200-line monster. I tried YAML files. They were hard to maintain.
I asked what I needed from an AI model.
- Send messages
- Get responses
- Handle errors
- Log usage
I built a thin adapter interface. I created a base class. Each provider gets its own adapter. Now my app does not care which provider is behind the scenes. I change the provider in one place.
This pattern is not a total fix. Tool calling and vision are harder. Providers use different schemas. I added optional arguments for images.
Start with an adapter pattern from day one. Write integration tests for each provider. Version your interface.
How do you keep your AI calls clean?
Source: https://dev.to/__c1b9e06dc90a7e0a676b/how-i-stopped-fighting-with-ai-apis-and-built-a-clean-integration-layer-58m5 Optional learning community: https://t.me/GyaanSetuAi