Technical documents differ from general text corpora in ways that complicate retrieval-augmented generation (RAG). Evidence for a single answer is often distributed across numbered clauses, tables, figures, captions, and ordered procedures rather than expressed in one passage. Standard RAG pipelines typically flatten these elements into independent chunks. This can break the document relations needed for exact evidence tracing. We introduce TechDocRAG, a relation-preserving framework for technical document question answering. The framework represents each document as a heterogeneous element graph and aligns three retrieval views for each element: technical identifiers, semantic summaries, and raw evidence. At query time, retrieval proceeds from identifier-aware recall to summary-level reranking and raw evidence bundling. We evaluate TechDocRAG on four benchmarks with more than 7500 evaluated question–answer pairs covering product manuals, engineering documents, and long multimodal PDFs. Across the suite, TechDocRAG improves the mean end-to-end score by 20.3 points over the strongest flat baseline and by 9.3 points over the strongest non-flat baseline. On the evidence-annotated subset, the strict raw evidence hit rate increases from 0.510 to 0.942. Resource profiling shows query time latency comparable to standard hybrid retrieval. Robustness tests show gradual degradation under relation loss, but clear sensitivity to severe identifier corruption. Overall, the results indicate that reliable RAG for technical documents depends less on retrieving more passages than on preserving the relations that make evidence interpretable.
Lee et al. (Wed,) studied this question.