Teams usually assume there’s a straightforward progression from prompt engineering through retrieval-augmented generation (RAG) to fine-tuning (the last rung on the ladder) when customizing large language models (LLMs). This is an easy-to-understand, frequently repeated narrative that is true for some developers but not for all teams working with LLMs in production environments.
Prompt engineering, RAG, and fine-tuning are not sequential upgrades in real-world enterprise systems. Instead, they represent different architectural methods for…
