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.
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