Retrieval-augmented generation (RAG) has become critical for groundbreaking large language models (LLMs) in enterprise knowledge, yet more than half of them failed in production due to retrieval latency or data issues. The root cause isn’t the LLM or embedding model used in RAG; it is due to treating RAG as an add-on instead of an integrated RAG, where retrieval and generation evolve together.
The Production RAG Crisis
The Promise vs. Reality
RAG is supposed to enhance the accuracy and relevance of LLMs by retrieving relevant context, augmenting the prompt, and generating grounded answers. It is designed to mitigate hallucinations, one of the most significant challenges facing large language models.