Retrieval Augmented Generation
- similar: Collaborative Customer Facing Data
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
- https://medium.com/the-modern-scientist/logical-retrieval-with-knowledge-graphs-the-key-to-contextual-and-intelligent-information-seeking-9fc51cc04ead Knowledge Graph
- https://generativeai.pub/lets-ditch-rag-f63faed1b96f
- https://ai.plainenglish.io/the-iterative-dance-of-the-rag-framework-for-knowledge-grounded-language-ai-e22a880b7159
- https://ai.plainenglish.io/elevating-rag-evaluation-the-synergy-of-faaf-and-ares-through-structured-output-be2e2556dfdd
- https://cobusgreyling.medium.com/an-ai-agent-architecture-framework-is-emerging-addae3804f23
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