RAG (Retrieval-Augmented Generation) and fine-tuning solve different problems. RAG grounds answers in documents you retrieve at query time. Fine-tuning changes model behavior by training on examples. Many production systems use both.
Choose RAG when
- Knowledge changes often (policies, product docs, tickets)
- You must cite sources or show evidence
- You need faster iteration without retraining cycles
Choose fine-tuning when
- You need consistent style, format, or domain language
- Prompting alone cannot stabilize behavior
- You have curated datasets and evaluation harnesses
Learn both with project proof
Compare Vector Skill Academy’s RAG System Development and LLM Fine-Tuning courses, or start from the Generative AI training institute hub for how these tracks fit together.
