# Assessment of RAG and Fine-Tuning for Industrial Question-Answering-Applications Source: [Assessment of RAG and Fine-Tuning for Industrial Question-Answering-Applications]() Collection index: [llms.txt]() Data updated: 2026-05-13T05:00:12.693Z Authors: Jakob Sturm, Josef Pichlmeier, Christian Bernhard, Maka Karalashvili, Johannes Klepsch, Georg Groh, Andre Luckow Published: 2026-05-10T13:35:16.000Z ## Abstract Large Language Models (LLMs) are increasingly employed in enterprise question-answering (QA) systems, requiring adaptation to domain-specific knowledge. Among the most prevalent methods for incorporating such knowledge are Retrieval-Augmented Generation (RAG) and fine-tuning (FT). Yet, from a cost-accuracy trade-off perspective, it remains unclear which approach best suits industry scenarios. This study examines the impact of RAG and FT on two closed datasets specific to the automotive industry, assessing answer quality and operational costs. We extend the Cost-of-Pass framework proposed by Erol et al. (arXiv:2504.13359) to jointly assess output quality, generation cost, and user interaction cost. Our findings reveal that while premium models perform best out of the box, open-source models can achieve comparable quality when enhanced with RAG. Overall, RAG emerges as the most effective and cost-efficient adaptation method for both closed- and open-source models. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()