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RAG vs. Fine-Tuning: A Decision Framework for Enterprise Teams

Vikram IyerPrincipal AI EngineerJun 30, 20266 min read

Summary

Teams routinely reach for fine-tuning when retrieval would be cheaper, safer, and easier to maintain. Here's how we decide.

Key takeaways

How to use this in real systems

  1. 1.Fine-tuning feels like the 'serious' option, but for most enterprise knowledge tasks, retrieval-augmented generation is faster to ship, cheaper to maintain, and easier to keep accu
  2. 2.We use a simple decision framework: if the task requires new knowledge, reach for RAG first. If it requires new behavior or style, consider fine-tuning. If it requires both, combin
  3. 3.This post walks through real examples from our client work, including where fine-tuning genuinely won out, and the retrieval architecture patterns we default to.

Article

Fine-tuning feels like the 'serious' option, but for most enterprise knowledge tasks, retrieval-augmented generation is faster to ship, cheaper to maintain, and easier to keep accurate as your underlying knowledge changes.

We use a simple decision framework: if the task requires new knowledge, reach for RAG first. If it requires new behavior or style, consider fine-tuning. If it requires both, combine them.

This post walks through real examples from our client work, including where fine-tuning genuinely won out, and the retrieval architecture patterns we default to.

RAGFine-TuningLLM Applications

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