RAG vs. Fine-Tuning: When to Use Each for Enterprise AI

One of the most common questions we hear from enterprise clients: "Should we fine-tune a model or use RAG?" The answer, as with most things in AI, is "it depends."

When to Use RAG

Retrieval-Augmented Generation is the right choice when:

• Your data changes frequently (policies, regulations, product catalogs)
• You need citations and source attribution for compliance
• Data sovereignty requires keeping information in your infrastructure
• You want to deploy quickly without expensive training cycles

When to Fine-Tune

Model fine-tuning makes more sense when:

• You need the model to adopt a specific communication style or domain vocabulary
• Tasks require deep pattern recognition that retrieval alone can't provide
• Latency is critical and you can't afford retrieval overhead
• Your use case is narrow and well-defined

The Hybrid Approach

In practice, the most effective enterprise AI deployments use both. Fine-tune a base model for your domain, then augment it with RAG for up-to-date information. This gives you the best of both worlds: deep domain understanding and current, citable information.

At Intenteon, our platform supports both approaches, and our AI Strategy team can help you determine the right mix for your specific needs.