Retrieval Augmented Generation

RAG helps bridge the gap for Small Language Models (SLMs)

  • While SLMs inherently manage other key aspects such as language generation and understanding, RAG equips them to perform comparably to their larger counterparts by enhancing their knowledge base.
  • This makes RAG a critical equalizer in the realm of AI language models, allowing smaller models to function with the robustness of a full-scale LLM.

Structured Output in Evaluation Frameworks

  • "As the field of Retrieval Augmented Generation continues to evolve, the adoption of structured output in evaluation frameworks will play a crucial role in advancing the state of the art."

References


Children
  1. GraphRAG

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