Title: RAG "Hype" vs. Reality
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: LLMs, RAG, Vector Databases, Finetuning

Abstract: Large Language Models (LLMs) require mechanisms to integrate external, specific, and
up-to-date knowledge beyond their static pre-training data. Retrieval-Augmented Gener-
ation (RAG) and finetuning represent two dominant paradigms to address this, but their
fundamental capabilities and long-term viability warrant critical evaluation. This position
paper argues that RAG, while offering practical utility for accessing dynamic information
and mitigating hallucination, constitutes a potentially overhyped approach with significant
inherent limitations fundamentally tied to its reliance on discrete retrieval steps. We con-
tend that RAG’s effectiveness is bottlenecked by retrieval quality, often leads to superficial
knowledge integration, struggles with complex reasoning requiring synthesis across informa-
tion pieces, and faces challenges in robustly leveraging long context windows. Furthermore,
the focus on auxiliary technologies like vector databases within the RAG ecosystem can
distract from core model capabilities. Conversely, we argue that finetuning, by directly
modifying the model’s parameters, enables deeper, more nuanced assimilation of domain
knowledge and task-specific skills. This parametric adaptation provides a more robust foun-
dation for complex reasoning and is crucial for unlocking true long-context understanding
and utilization within the model itself. While acknowledging finetuning’s computational
and data requirements, we conclude that it offers a more powerful and durable pathway
towards developing truly specialized, knowledgeable, and context-aware LLMs, positioning
it as the cornerstone for advancing LLM capabilities beyond the architectural constraints
of current RAG systems

TL;DR: Finetuning as the Cornerstone for Deep Knowledge and Long Context in LLMs