AI-Friendly LaTeX: Using LaTeX Code as a Knowledge Source for Retrieval-Augmented Generation
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Computer Science > Information Retrieval
Title:AI-Friendly LaTeX: Using LaTeX Code as a Knowledge Source for Retrieval-Augmented Generation
Abstract:Large language models can answer questions about textbooks, lecture notes, and programming exercises more reliably when their answers are grounded in an explicit knowledge source. Retrieval-augmented generation (RAG) is a common approach: relevant fragments of a document are retrieved and inserted into the model context before answering. For mathematical and technical material, the original LaTeX source can be a better starting point than a PDF, because it contains structural information, labels, sectioning commands, macros, and authorial intent that are often lost or distorted in PDF extraction. However, LaTeX source is not automatically AI-friendly. Cross-references must be resolved, custom macros must be interpreted, exercises and examples must be identified, and author-supplied semantic metadata may be needed. This article describes a focused preprocessing approach for turning LaTeX source, together with its compiled auxiliary files and optional author annotations, into Markdown and JSONL chunks suitable for indexing in a vector database.
| Comments: | 19 pages, 3 figures |
| Subjects: | Information Retrieval (cs.IR); Computation and Language (cs.CL) |
| ACM classes: | H.3.3; H.3.1; I.7.2; I.2.7 |
| Cite as: | arXiv:2605.22923 [cs.IR] |
| (or arXiv:2605.22923v1 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2605.22923
arXiv-issued DOI via DataCite (pending registration)
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