Have you embraced the RAG hype and now find yourself navigating its challenges?

Retrieval Augmented Generation (RAG) has become a popular entry point into the AI world for many engineers. Its appeal lies in the simplicity of building a prototype using LangChain, coupled with the promise of impressive results.

However, when transitioning to production, certain properties of RAG can introduce unpredictability.

Recently, I’ve been working on a RAG-based chatbot. While we swiftly developed a proof of concept, evaluating its performance proved to be a complex task. We discovered that the LLM struggled with consistency—sometimes disregarding critical prompt instructions entirely. Moreover, the responses varied significantly between calls.

Adding more instructions to the prompt to address these issues often exacerbated the problem, leading to even more erratic behavior.

While RAG is a fantastic starting point, it appears that fine-tuning is essential to mitigate these challenges.

What has been your experience with RAG-based systems? Have you encountered similar issues?

#RAG #AI #ML #LLM #Chatbot