Has the time finally come for Config and Code to come together for developing Gen AI applications?
For countless years, we've had numerous debates about whether ML should be more config-driven or more code-driven. There are pros and cons to both:
| Attribute | Code | Config |
|---|---|---|
| Iteration speed | Con: usually slower iteration speed especially as the code base gets more complicated | Pro: faster to iterate and adjust configurations |
| Debugging | Pro: easier to identify and fix issues within a single place | Con: can be difficult to navigate in between code and config to isolate issues |
| Maintenance | Con: code maintenance can become complex without clean organization and abstractions | Pro: configs are already abstractions of critical settings of the application |
| Dependency Management | Pro: libraries and dependencies are usually better managed | Con: dependency issues are more common due to implicit dependencies |
Given the nature of Generative AI enabling more model settings, parameters, and prompts requiring to be iterated upon, it's opened up the opportunity for Code + Config to come together to enable a better Gen AI developer experience.
We open-sourced a library, AI Config, that blends the experience together to create a seamless way to develop GenAI applications while managing GenAI components within a configuration. Let us know what you think!
Top comments (1)
⚙️ configs