Practical tips for retrieval-augmented generation (RAG)
Retrieval-augmented generation (RAG) is an effective method for specializing LLMs with your data, requiring more than combining pretrained models.
MAIN POINTS
- RAG effectively specializes LLMs over your own data.
- Successful RAG application involves more than just combining pretrained models.
- RAG is one of the easiest ways to customize LLMs.
TAKEAWAYS
- RAG requires additional steps beyond model combination for success.
- Specializing LLMs with RAG is highly effective.
- The process of RAG is user-friendly.