Intelligent Query Reformulation for Enhanced Retrieval Precision and Generation Faithfulness in Dense Terminology Domains

In this paper, we introduce Technical-Embeddings, a novel framework designed to optimize semantic retrieval in technical documentation, with applications in both hardware and software development. Our approach addresses the challenges of understanding and retrieving complex technical content by leveraging the capabilities of Large Language Models (LLMs). First, we enhance user queries by generating expanded representations that better capture user intent and improve dataset diversity, thereby enriching the fine-tuning process for embedding models. Second, we apply summary extraction techniques

Research goal: To what extent does intelligent query reformulation improve retrieval precision and final generation faithfulness in dense terminology domains compared to standard RAG pipelines?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.2/10.