RAG was introduced by Meta AI in 2020 as a method to improve Large Language Model (LLM) accuracy by grounding responses in retrieved, external data.
The shift toward systems that refine queries iteratively allows for better handling of complex, multi-document synthesis tasks. eccentric_rag_2020_remaster
Techniques such as Concept Bottleneck Models (CBM-RAG) are being applied to improve the interpretability of retrieved evidence, particularly in specialized fields like medical report generation. 4. Challenges and Future Directions RAG was introduced by Meta AI in 2020
Recent developments emphasize modular pipelines and better evaluation protocols, moving away from simple "retrieve-and-generate" approaches. 2. Core Advantages of Modern RAG outdated knowledge within parametric-only models.
To reduce hallucination rates and overcome the limitations of static, outdated knowledge within parametric-only models.