Making RAG work well for a chatbot
When RAG is added on top of existing for a chatbot, the result depends heavily on quality. The basic include splitting documents into useful chunks so important context is not lost, using reliable and relevant data sources, and repeatedly testing results so the setup can be improved. In tests for a niche domain, broad tools such as ChatGPT and Gemini sometimes gave better answers than a custom .
That gap may come from the much wider data and knowledge already available inside those large systems. The practical goal is not to beat those services, but to find techniques that bring a custom system closer to useful real-world .
Key points
- The goal is to use existing with RAG in a chatbot.
- Good document chunks help preserve the context needed for accurate answers.
- Data sources need to be high quality and closely related to the task.
- The system should be tested and improved continuously.
- In a niche domain, ChatGPT and Gemini may still outperform a custom RAG setup.