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RAG continues to rise
Practical AI: Machine Learning, Data Science, LLM
The Debate on RAG vs Fine Tuning in Highly Specialized Fields
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In highly specialized fields, the choice between RAG and fine tuning depends on the level of expertise required. Fine tuning is suitable for achieving a specific form of output, such as custom functions like GPT functions. It is emphasized that fine tuning is essential when using a small model, particularly in domain-specific applications. However, opting for a small, fine-tuned model requires a dedicated team for support, likening it to playing on 'hard mode'. Many enterprise customers initially believe in the necessity of fine tuning but realize its dispensability after solving use cases without it. It is recommended to first validate the use case using an easy API before delving into the complexities of fine-tuning with GPUs and model servers. Additionally, fine-tuning may be considered later when sufficient data is available. The advent of OpenAI API enables quick validation of ideas, potentially leading to the exploration of open-source or smaller models like GPT-3.5 Turbo to achieve similar results as more advanced models like GPT-4.
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