The Customization Decision
Every enterprise deploying LLMs faces the same question: should we fine-tune a model on our data, or use Retrieval-Augmented Generation (RAG) to inject context at query time?
Fine-Tuning: Best For
- Consistent style and tone across outputs
- Domain-specific terminology and reasoning
- Low-latency responses at scale
- Tasks where the knowledge base rarely changes
RAG: Best For
- Rapidly changing knowledge bases
- Traceable, source-cited responses
- Multi-domain queries across large document sets
- Compliance-sensitive contexts requiring auditability
The Hybrid Approach
At uFlo.ai, we typically deploy fine-tuned models for core domain reasoning with RAG for real-time knowledge retrieval. This gives you the best of both worlds: domain expertise plus current information.