Components of a RAG system
RAG enables language models to ground their outputs in external knowledge, providing an elegant solution to the limitations that plague pure LLMs: hallucinations, outdated information, and restricted context windows. By retrieving only relevant information on demand, RAG systems effectively bypass the context window constraints of language models, allowing them to leverage vast knowledge bases without squeezing everything into the model’s fixed attention span.
Rather than simply retrieving documents for human review (as traditional search engines do) or generating answers solely from internalized knowledge (as pure LLMs do), RAG systems retrieve information to inform and ground AI-generated responses. This approach combines the verifiability of retrieval with the fluency and comprehension of generative AI.
At its core, RAG consists of these main components working in concert:
- Knowledge base: The storage layer for external information ...
