Source: Creating RAG-Based Question-and-Answer LLM Workflows at NVIDIA
The rapid development of solutions using retrieval augmented generation (RAG) for question-and-answer LLM workflows has led to new types of system architectures. NVIDIA work, using AI for internal operations, has led to several important findings for finding alignment between system capabilities and user expectations.
NVIDIA found that regardless of the intended scope or use case, users generally want to be able to execute non-RAG tasks like performing document translation, editing emails, or even writing code. A vanilla RAG application might be implemented so that it executes a retrieval pipeline on every message, leading to excess usage of tokens and unwanted latency as irrelevant results are included.
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