01 · Basics
RAG retrieves knowledge; fine-tuning changes habits
RAG searches enterprise knowledge before generating an answer, which makes it easier to update and audit.
Fine-tuning changes output behavior through examples. It can help with style and formats, but it does not solve messy knowledge.
02 · First Step
Organize knowledge before training models
Many companies ask about training their own model too early. The real bottleneck is often unclear documents, permissions, and acceptance standards.
If the knowledge source is inconsistent, fine-tuning only makes the inconsistency harder to debug.
03 · Framework
Use four questions to decide
Does the knowledge update often? Must answers show sources? Are there many high-quality labeled examples? Does output depend heavily on style and format?
RAG wins on the first two; fine-tuning becomes relevant on the latter two.
- Policies and manuals: RAG
- Classification and fixed formats: evaluate fine-tuning
- Knowledge plus fixed format: combine RAG with templates
04 · Maintenance
The real cost is ongoing maintenance
RAG requires document updates, index quality, permissions, and answer evaluation. Fine-tuning requires dataset quality, versioning, regression tests, and drift monitoring.
For most SMEs, a RAG MVP is the fastest way to validate value.