Retrieval-Augmented Generation for Urdu Islamic Jurisprudence: A Review of Design Choices and a Reference Architecture
Abstract
Questions about Islamic law need exact answers with sources. A wrong citation is a serious failure. Retrieval-augmented generation (RAG) fits this need, but Urdu is a low-resource language and there is little guidance for building such a system. This paper reviews the design choices behind an Urdu Fatawa RAG system and proposes a reference architecture. We compare Urdu-capable language models, embedding models, tokenizer cost, vector databases, primary data stores, and backend frameworks against four requirements: faithfulness to sources, precision on close jurisprudential terms, Urdu fluency, and private self-hosting. We argue for chunk-level storage of question-answer pairs, hybrid dense and sparse retrieval fused by reciprocal rank fusion, a prompt with English instructions and an Urdu output example, and a three-container deployment that separates inference, vector search, and the API. Our choices rest on published benchmarks, not on a controlled comparison on our own corpus, and some remain hypotheses. We state which ones, and we give an evaluation plan.
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BibTeX
@misc{hussain2026retrievalaugmented,
title = {{Retrieval-Augmented Generation for Urdu Islamic Jurisprudence: A Review of Design Choices and a Reference Architecture}},
author = {Abis Hussain},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23560150},
url = {https://zenodo.org/records/23560150},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Retrieval-Augmented Generation for Urdu Islamic Jurisprudence: A Review of Design Choices and a Reference Architecture
AU - Abis Hussain
PY - 2026
DA - 2026/10/22
PB - Zenodo
DO - 10.5281/zenodo.23560150
UR - https://zenodo.org/records/23560150
AB - Questions about Islamic law need exact answers with sources. A wrong citation is a serious failure. Retrieval-augmented generation (RAG) fits this need, but Urdu is a low-resource language and there is little guidance for building such a system. This paper reviews the design choices behind an Urdu Fatawa RAG system and proposes a reference architecture. We compare Urdu-capable language models, embedding models, tokenizer cost, vector databases, primary data stores, and backend frameworks against four requirements: faithfulness to sources, precision on close jurisprudential terms, Urdu fluency, and private self-hosting. We argue for chunk-level storage of question-answer pairs, hybrid dense and sparse retrieval fused by reciprocal rank fusion, a prompt with English instructions and an Urdu output example, and a three-container deployment that separates inference, vector search, and the API. Our choices rest on published benchmarks, not on a controlled comparison on our own corpus, and some remain hypotheses. We state which ones, and we give an evaluation plan.
ER -