RAG for Requirements: Teaching LLMs to Trace and Reason(NPO-53610-1)

Requirement traceability, validation, and verification can become difficult within engineering projects, notably as they scale. Technical specification documents detailing these structured processes are primarily expressed using natural language. With the adoption of Large Language Models (LLMs) and their effectiveness in natural language processing tasks such as information and relationship extraction, specification documents can be leveraged. While traditional requirement and test engineering methods rely on human thinking, smaller models can perform goal-aligned reasoning when trained with human feedback. We propose an approach for requirement traceability using Light Retrieval-Augmented Generation (RAG) by fine-tuning an LLM for improved knowledge discovery, primarily entity-relationship extraction. Our approach seeks to induce traceability, which emerges from the knowledge graph component of the RAG system by evaluating responses with human expert feedback for alignment.