One of the most frequent questions we receive from our clients is: should we fine-tune a model or implement RAG? The answer, as always in technology, is "it depends."
When to use RAG
RAG (Retrieval Augmented Generation) is ideal when: - Your knowledge base changes frequently - You need to cite specific sources - The data volume is very large - You require transparency in responses
When to use Fine-tuning
Fine-tuning is preferable when: - You need very specific behavior - The output format must be consistent - Latency is critical - Knowledge is stable
Hybrid Approach
In practice, many enterprise solutions combine both approaches. A fine-tuned model for format and style, with RAG for up-to-date knowledge.