Which is more effective: retrieval augmentation (RAG) or fine-tuning? The synergy of both.

The emergence of ChatGPT in November 2022 brought significant changes to the AI landscape. Following this, corporate leaders advocated for the adoption of large language models (LLMs), prompting data science teams to explore the realms of fine-tuning and retrieval-augmented generation (RAG) to address generative AI (genAI) limitations.

Within the data science community, there is often a discourse on which approach delivers superior results. The answer lies in a harmonious blend of both. Fine-tuning and RAG are not mutually exclusive; in fact, their synergy enhances overall performance.

To draw an analogy, think of a doctor needing specialized training (fine-tuning) and access to a patient’s medical chart (RAG) to make an accurate diagnosis.

Let’s delve into the mechanics of each approach and understand why treating them as collaborative tools yields more effective outcomes than pitting them against each other.

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