Prompting vs. RAG vs. fine-tuning: Why it’s not a ladder

Teams commonly view prompt engineering, RAG, and fine-tuning as a progressive ladder when customizing large language models, but this linear approach often leads to production failures. Instead of sequential upgrades, these represent different architectural methods that address distinct problems and constraints. A six-dimensional framework evaluating data privacy, latency, control, update frequency, deployment target, and cost provides better guidance for LLM architecture decisions, with successful production systems typically using hybrid approaches rather than single methods.