To reduce hallucination rates and overcome the limitations of static, outdated knowledge within parametric-only models.
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. 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 To reduce hallucination rates and overcome the limitations
Implementing sophisticated RAG systems introduces significant technical complexity and computational costs. diversifying into hybrid retrievers
The field has moved beyond basic RAG, diversifying into hybrid retrievers, iterative retrieval loops, and graph-based retrieval systems.
It eliminates the need for expensive, frequent model fine-tuning.