RAG vs. Fine-Tuning: When to Use Each for Your Enterprise AI
RAG retrieves your documents at query time — best for dynamic, frequently-updated knowledge bases. Fine-tuning bakes your data into the model's weights — best for consistent tone, format, and domain behavior. ConsultingWhiz helps enterprises choose and implement the right architecture, typically delivering production-ready systems in 3–6 weeks.
RAG and fine-tuning both customize LLMs for enterprise use — but they solve different problems. This guide explains when to use each and when to combine both.