Should travel apps use RAG instead of fine-tuning?

For travel apps, retrieval-augmented generation (RAG) is recommended over fine-tuning for frequently changing facts, with fine-tuning reserved for stable behavior, according to a technical guide referencing Hugging Face documentation. The guide advises using RAG for dynamic information like prices and availability, retrieved at request time with citations, while fine-tuning adapts models to task-specific datasets for consistent behavior. A practical production system often combines both approaches.

For a travel app, I would start with RAG for changing facts and use fine-tuning only if you later need to change the model’s behavior. Use RAG for information such as: Those facts change frequently, so they should stay in databases/APIs or an indexed knowledge base. Retrieve them at request time, include source timestamps, and show citations. Hugging Face’s RAG documentation describes the same basic pattern: retrieve documents and pass them to the generator: RAG · Hugging Face https://huggingface.co/docs/transformers/model doc/rag Fine-tuning is better for stable behavior, for example: Fine-tuning adapts a pretrained model to a task-specific dataset, but it is not a convenient replacement for a frequently updated source of truth: Fine-tuning · Hugging Face https://huggingface.co/docs/transformers/en/training A practical architecture is therefore: So the short answer is: RAG first; fine-tuning later if needed; often the best production system uses both.