RAG Series (1): Why LLMs Need External Memory
Large language models (LLMs) often produce hallucinations or fail to answer questions due to their static knowledge, which is fixed at training time. Retrieval-Augmented Generation (RAG) addresses this by dynamically retrieving up-to-date information from external sources during inference. RAG separates knowledge storage from language generation, enabling more accurate and current responses without retraining the model.
- ▪LLMs have a knowledge cutoff and cannot access real-time or private data, leading to hallucinations when they fabricate answers.
- ▪Fine-tuning is ineffective for injecting new factual knowledge into LLMs and does not scale well for dynamic data.
- ▪RAG improves accuracy by retrieving relevant information at query time and injecting it into the prompt.
- ▪Long context methods can work for small, static document sets, but RAG is better suited for large, frequently updated enterprise knowledge bases.
- ▪The RAG pipeline consists of an offline indexing phase and a real-time query phase, both relying on a shared vector database.
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