# RAG vs Fine-Tuning: What Are You Actually Using in Production LLM Apps?

> Source: <https://discuss.huggingface.co/t/rag-vs-fine-tuning-what-are-you-actually-using-in-production-llm-apps/178943#post_1>
> Published: 2026-08-20 06:19:53+00:00

There is a lot of discussion around whether production LLM applications should rely on RAG, fine-tuning, prompt engineering, or increasingly capable base models.

But the answer becomes less obvious when dealing with real business data.

For example:

**RAG** can provide current and private information but introduces retrieval quality, chunking, ranking, and latency challenges.

**Fine-tuning** can improve specialized behavior and domain performance, but maintaining updated knowledge can become difficult.

**Prompt engineering** is faster to implement but may become fragile as applications grow.

And increasingly, smaller specialized models can sometimes perform surprisingly well at a fraction of the inference cost.

For people building production systems:

**Where do you currently draw the line between RAG and fine-tuning?**

I’m especially interested in experiences involving:

What has actually worked for you beyond the prototype stage?