When Do Large Language Models Need Retrieval? A Comparative Study of RAG, Fine-Tuning, and Hybrid Adaptation Strategies

Abstract

Large language models (LLMs) have achieved strong performance across a broad range of natural language processing tasks and are increasingly deployed in domain- specific settings such as biomedical question answering and open- domain information access. However, adapting LLMs to spe- cialized domains remains challenging due to domain knowledge gaps, evolving information, and computational constraints. Two primary adaptation strategies are commonly used: fine-tuning, which internalizes domain knowledge within model parameters, and retrieval-augmented generation (RAG), which incorporates external evidence at inference time. Hybrid approaches that combine fine-tuning with retrieval have also been proposed, yet their relative trade-offs remain insufficiently characterized under controlled conditions.
In this work, we present a systematic empirical comparison of fine-tuning, RAG, and hybrid adaptation strategies using a unified evaluation framework. We analyze these approaches across multiple dimensions, including answer quality, grounding reliability, inference latency, and computational cost. Our study highlights practical trade-offs between internalized and external knowledge integration and provides decision-oriented guidelines for selecting adaptation strategies in real-world deployments. Rather than assuming a universally optimal approach, our results emphasize that the need for retrieval depends on domain characteristics, data availability, and system constraints.