I recently did a deep dive into Chip Huyen's "AI Engineering" and one argument stood out as particularly crucial for teams building with LLMs today. It's the clear, principled distinction between Retrieval-Augmented Generation (RAG) and finetuning.

Many engineering teams instinctively reach for finetuning as a way to "teach" a model their private data. The thinking is that if you train it on your documents, it will "know" them. However, this is often a misuse of the technique. Finetuning is most effective at altering the form and behavior of a model—making it communicate in a certain style, adhere to a specific JSON schema, or follow a complex chain of instructions. It is an expensive and imprecise tool for knowledge injection.

The book argues that RAG is the superior tool for providing facts. By retrieving relevant information from an external knowledge base at inference time and adding it to the prompt context, you get several advantages:

Factual Grounding: The model is less likely to hallucinate because its context is bounded by the retrieved documents.

Traceability: You know exactly which source documents were used to generate an answer.

Up-to-date Knowledge: The knowledge base can be updated continuously without the cost of retraining/finetuning the model itself.

The core takeaway is that teams should default to RAG for knowledge-based tasks and reserve the more complex and expensive process of finetuning for tasks that require altering the model's fundamental behavior. This seems like a critical architectural decision that could save significant resources. Curious to hear how others are approaching this trade-off.