The Importance of Fine Tuning and RAG in Real World AI

Fine tuning and tweaking are crucial in achieving desirable results from a RAG type system. The ultimate goal in real world AI is to effectively use diverse data in a general AI conversational experience. This goes beyond simple chat interactions to include multimodal capabilities and dynamically generating graphical user interfaces. Real world AI, especially in business use cases, requires the incorporation of live, dynamic and often confidential or proprietary data, which doesn't get fine-tuned into the model but is utilized in the infrastructure around the model. This necessitates the use of RAG or its variants rather than relying solely on fine tuning. A debate between RAG and fine tuning emerged, with the understanding that training a model on sensitive data can lead to privacy breaches, highlighting the significance of a broader, more applied approach in AI.