Analyzing Quality-Latency-Resource Trade-offs in a Technical Documentation RAG Assistant Using LoRA Adaptation
arXiv:2605.28222v1 Announce Type: cross Abstract: We study quality-latency-resource trade-offs in a documentation-grounded retrieval-augmented generation (RAG) system that uses Low-Rank Adaptation (LoRA) of the generator. We build a manually verified benchmark of 5,144 question-answer pairs over the official Kubernetes documentation and combine it with a fixed hybrid-retrieval pipeline (BGE-M3 dense, BGE-M3 native sparse, Reciprocal Rank Fusion, cross-encoder reranking). Over this benchmark we ablate 20 LoRA configurations on Llama-3.2-3B-Instruct and Llama-3.1-8B-Instruct across rank and targ
The proliferation of complex AI models creates an imperative to optimize for efficiency without sacrificing performance, making fine-tuning methods like LoRA crucial now.
This research provides a framework for understanding the critical trade-offs between AI model quality, operational latency, and computational resource consumption, which directly impacts deployment strategies and cost.
The ability to systematically analyze and optimize LoRA configurations for specific applications, like RAG systems, improves the practical viability and cost-effectiveness of custom AI solutions.
- · Companies deploying RAG systems
- · Cloud providers with optimized infrastructure
- · AI researchers focused on efficiency
- · Developers of custom AI agents
- · Companies relying on unoptimized large models
- · Infra providers without efficient serving options
More efficient and cost-effective deployment of specialized AI models in enterprise environments.
Accelerated adoption of RAG systems for knowledge retrieval and content generation across various industries.
Enhanced competition among AI model developers to deliver increasingly optimized and domain-specific solutions, potentially leading to fully autonomous specialized AI agents.
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Read at arXiv cs.LG