Separating LLM reasoning from knowledge using local models and web search
A user on the r/LocalLLaMA community inquired about the feasibility of using a lightweight local large language model (LLM) strictly for reasoning and comprehension, while outsourcing its knowledge retrieval to external web search tools. This approach highlights a growing interest in decoupling reasoning from static training data, potentially allowing resource-constrained local devices to leverage up-to-date information without needing massive parameter sizes. The proposed setup relies on concepts like Retrieval-Augmented Generation (RAG) and Search-Augmented Generation, where the local LLM acts as the "brain" to process search results rather than relying on its internal memory.
## BACKGROUND
Retrieval-Augmented Generation (RAG) is a technique that optimizes LLM outputs by querying external databases or web sources before generating a response. This reduces hallucinations and the need for constant retraining, making it highly suitable for lightweight models that lack extensive built-in knowledge bases.