Q: What is the difference between Fine Tuning and RAG? Fine Tuning:

Fine Tuning is a process that adjusts a pre-trained model to work better for a specific task or dataset. It involves making small changes to the model's settings and weights. This process helps the model learn from new data and improve its performance. Fine Tuning is useful when there is limited data available for training, and it helps create accurate and efficient models that meet specific needs. Fine Tuning is a process of adjusting the parameters of a model to improve its performance on a specific task

Few more important things about Fine Tuning:

1. Fine Tune is very expensive
2. Fine Tune demands very high computational cost.
3. Changes that are done during fine tuning are available to everyone with the access of ChatGpt
4. Fine Tuning adjusts the model's parameters, while RAG adds an external knowledge retrieval component.

Retrieval-Augmented Generation (RAG):

Retrieval-Augmented Generation (RAG) is a natural language processing technique. It fetches relevant information from a knowledge source and uses it to augment the output of a pre-trained language model. This integration enhances the accuracy, informativeness, and contextual relevance of generated text. RAG leverages the strengths of both retrieval and generation models to produce superior results.

Few more important things about RAG:

1. RAG is less expensive
2. RAG does not need higher system to run as Fine Tune need.
3. Changes that are done during RAG are only available to those with the access of your own Custom GPT.
4. RAG adds an external knowledge retrieval component.