I’m probably jumping ahead a bit, but I’m curious about the benefits of using a Retriever versus allowing the LLM to retrieve information. Module 1 specifies the benefits of using the Retriever from a role separation perspective, but I’m curious if you let the LLM also act as the Retriever if it also goes through the same steps including the ranking of relevant information? I know the technology is rapidly changing and am wondering if it’s still better to build a retriever to perform that function or if the LLM is already great at doing that?
Using a specialized Retriever (RAG) instead of relying solely on an LLM offers crucial benefits through role separation - Retriever effeciently searches, filters, ranks vast amount of external data sources while the LLM focus is more on generation. This ensures better accuracy, reduces hallucinations, handles dynamic data, and optimizes token costs.
1.Dedicated retrievers excel at semantic search and retrieving relevant documents (Top-K chunks), whereas LLMs are meant for reasoning and generation, not acting as an efficient database lookup tool.
2.Retrievers ground LLM responses in trusted, external sources (RAG) which improves factual accuracy compared to relying on an LLM’s parametric memory.
3.Retrievers can search up-to-date knowledge bases, addressing the challenge of static LLM training data without needing expensive, time-consuming retraining or fine-tuning. Remember the earlier LLM always mentions on which data (year), it was trained and the response of the LLM might be based upon the same.
4.Instead of sending massive datasets to the LLM, a retriever selects only the most relevant subset (context), lowering token usage and reducing cost as well.
Ofcourse with a refined and updated LLM one can get better responses but having a specialised retriever have high-recall and high-precision in large scale applications.
Regards
Dr. Deepti