Uploaded August 2025 | Updated September 2026, 2 weeks ago

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Raja Iqbal sits down with Jay Alammar to discuss one of the most important choices in enterprise AI: when to use Retrieval-Augmented Generation (RAG) vs. fine-tuning.

In this clip, we dive into why RAG is often the preferred starting point for grounding models in specific datasets—especially in scalable, cost-effective enterprise applications.

You’ll learn:

- Why RAG is cheaper, faster to update, and more flexible
- When fine-tuning actually makes sense (deep domain, specific formats, internal jargon)
- How prompt engineering, RAG, and fine-tuning fit together in a practical AI stack

Perfect for ML engineers, product leaders, and anyone building real-world AI systems.

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