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.
This report provides an overview of the landscape following its introduction in 2020, based on systematic literature reviews published through 2025. 1. Executive Summary: RAG Evolution (2020–2025) eccentric_rag_2020_remaster
As RAG techniques become more fragmented, developing unified protocols for evaluation is crucial for ongoing development. 5. Conclusion RAG was introduced by Meta AI in 2020
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 eccentric_rag_2020_remaster