mlsanityData to 7 Oct 2026

Towards AI Evaluation in Domain-Specific RAG Systems: The AgriHubi Case Study

arXiv 2602.02208 ↗First posted 2 Feb 2026cs.CL cs.AI cs.IRcited by 0

On the map: Retrieval-augmented generation → Time series and agricultural RAG

Abstract (arXiv)

Large language models show promise for knowledge-intensive domains, yet their use in agriculture is constrained by weak grounding, English-centric training data, and limited real-world evaluation. These issues are amplified for low-resource languages, where high-quality domain documentation exists but remains difficult to access through general-purpose models. This paper presents AgriHubi, a domain-adapted retrieval-augmented generation (RAG) system for Finnish-language agricultural decision support. AgriHubi integrates Finnish agricultural documents with open PORO family models and combines explicit source grounding with user feedback to support iterative refinement. Developed over eight iterations and evaluated through two user studies, the system shows clear gains in answer completeness, linguistic accuracy, and perceived reliability. The results also reveal practical trade-offs between response quality and latency when deploying larger models. This study provides empirical guidance for designing and evaluating domain-specific RAG systems in low-resource language settings.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Time series and agricultural RAG, one of 15 directions in Retrieval-augmented generation. Builds on 10 of its 16 references that are in the map. First posted on arXiv 2 Feb 2026.

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10 of 16 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025AgriRegion: Region-Aware Retrieval for High-Fidelity Agricultural Advice10 Dec
  2. 2025AgriGPT: a Large Language Model Ecosystem for Agriculture12 Aug
  3. 2025AgroLLM: Connecting Farmers and Agricultural Practices through Large Language Models for Enhanced Knowledge Transfer and Practical Application28 Feb

Other references · 7

  1. 2024LLMs Beyond English: Scaling the Multilingual Capability of LLMs with Cross-Lingual Feedbackcited by 51
  2. 2023Generative Artificial Intelligence for Software Engineering -- A Research Agendacited by 23
  3. 2024Poro 34B and the Blessing of Multilingualitycited by 18
  4. 2025A Systematic Literature Review of Retrieval-Augmented Generation: Techniques, Metrics, and Challengescited by 16
  5. 2024ShizishanGPT: An Agricultural Large Language Model Integrating Tools and Resourcescited by 9
  6. 2024A Survey of Large Language Models for European Languagescited by 6
  7. 2025Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluationcited by 3

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  1. 2024RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
  2. 2026Cost-Efficient Cross-Lingual Retrieval-Augmented Generation for Low-Resource Languages: A Case Study in Bengali Agricultural Advisory
  3. 2026AgriIR: A Scalable Framework for Domain-Specific Knowledge Retrieval
  4. 2026Where Does Retrieval Fail? Evaluating RAG Architectures for Agricultural Advisory
  5. 2025Optimizing Agricultural Research: A RAG-Based Approach to Mycorrhizal Fungi Information
  6. 2025Evaluating Small Language Models for Agentic On-Farm Decision Support Systems
  7. 2024Developing Retrieval Augmented Generation (RAG) based LLM Systems from PDFs: An Experience Report
  8. 2025Towards Large Reasoning Models for Agriculture