Config vs. Code in Gen AI

#config #ai #mlops

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!

Github repo

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