Hi kirti yadav
In traditional large language models (LLMs), data is embedded during the pretraining phase, where the model learns statistical patterns from vast corpora and encodes this knowledge into its parameters. At runtime, the model does not access external data sources; instead, it generates responses based solely on its internalized knowledge. This means that once trained, the model cannot dynamically incorporate new or domain-specific information unless it is fine-tuned or retrained.
In contrast, Retrieval-Augmented Generation (RAG) systems introduce a hybrid architecture where the model can access external knowledge bases during inference. In a RAG pipeline, data is first embedded into vector representations and stored in an index. When a user query is received, the system retrieves the most relevant documents from this index using similarity search, and these documents are then passed along with the query to the LLM to generate a response. This allows RAG systems to provide more accurate and up-to-date answers without retraining the model
Reference : Evaluate RAG with LlamaIndex
Architecturally, the key difference lies in how information is accessed: traditional LLMs rely entirely on static, pre-embedded knowledge, while RAG systems dynamically retrieve and incorporate external data at runtime. Advanced RAG frameworks like DRAGIN further enhance this by allowing the model to decide when and what to retrieve based on its internal information needs during generation, making the process more adaptive and context-aware.
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