🎙️ RAG vs. Fine Tuning - Dive into the latest episode of "Grounded Truth" hosted by John Singleton as he discusses "Retrieval Augmented Generation (RAG) versus Fine Tuning in LLM Workflows" with Emmanuel Turlay, Founder & CEO of Sematic and Airtrain.ai and Shayan Mohanty, Co-founder & CEO of Watchful.

🤖 RAG: Retrieval Augmented Generation - RAG involves putting content inside the prompt/context window to make models aware of recent events, private information, or company documents.

The process includes retrieving the most relevant information from sources like Bing, Google, or internal databases, feeding it into the model's context window, and generating user-specific responses.

Ideal for ensuring factual answers by extracting data from a specified context.

⚙️ Fine Tuning - Fine tuning entails training models for additional epochs on more data, allowing customization of the model's behavior, tone, or output format.

Used to make models act in specific ways, such as speaking like a lawyer or adopting the tone of Harry Potter.

Unlike RAG, it focuses more on the form and tone of the output rather than knowledge augmentation.

🤔 Decision Dilemma: RAG or Fine Tuning?

Emmanuel highlights the misconception that fine tuning injects new knowledge, emphasizing its role in shaping the output according to user needs.

RAG is preferred for factual answers, as it extracts information directly from a specified context, ensuring higher accuracy.

Fine tuning, on the other hand, is more about customizing the form and tone of the output.

🔄 The Verdict: A Balanced Approach?

It's not a one-size-fits-all decision. The choice between RAG and fine tuning depends on specific use cases.

Evaluating the decision involves understanding the goals: knowledge augmentation (RAG) or customization of form and tone (Fine Tuning).

Perhaps a balanced approach, leveraging both techniques based on the desired outcomes.

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