The Most Common Architecture Decision in Enterprise AI
When organizations set out to build bespoke domain-specific intelligence, engineering leadership inevitably confronts the core dilemma: Fine-Tuning (LoRA/QLoRA) or Retrieval-Augmented Generation (RAG)?
Decision Framework
- Choose RAG When: Data updates frequently (news, stock prices, customer CRM), citations are legally mandated, or budgets require rapid zero-training deployment.
- Choose Fine-Tuning When: Teaching specific output formats, specialized vocabularies (radiology, specialized code frameworks), or reducing prompt token costs by embedding instructions directly into weights.
- Hybrid Architecture: The industry consensus is to fine-tune a compact model on style and reasoning structure, while utilizing RAG to inject dynamic facts at inference time.