Derivation Prompting: A Logic-Based Method for Improving Retrieval-Augmented Generation

2026-05-13

AI Summary

This work addresses the susceptibility of large language models to hallucination and erroneous reasoning in knowledge-intensive question answering. To mitigate these issues, the authors propose a structured prompting method inspired by formal logical deduction, integrated within a retrieval-augmented generation (RAG) framework. The approach employs predefined rules to systematically construct interpretable derivation trees from initial hypotheses, thereby enabling controllable and traceable reasoning pathways. By introducing formal logical mechanisms into prompt engineering—a novel contribution—the method significantly reduces the proportion of unacceptable responses in case studies, outperforming both conventional RAG systems and approaches relying on extended context windows.

Abstract

The application of Large Language Models to Question Answering has shown great promise, but important challenges such as hallucinations and erroneous reasoning arise when using these models, particularly in knowledge-intensive, domain-specific tasks. To address these issues, we introduce Derivation Prompting, a novel prompting technique for the generation step of the Retrieval-Augmented Generation framework. Inspired by logic derivations, this method involves deriving conclusions from initial hypotheses through the systematic application of predefined rules. It constructs a derivation tree that is interpretable and adds control over the generation process. We applied this method in a specific case study, significantly reducing unacceptable answers compared to traditional RAG and long-context window methods.

Problem

• hallucinations
• erroneous reasoning
• knowledge-intensive tasks
• domain-specific tasks
• question answering

Innovation

• Derivation Prompting
• Retrieval-Augmented Generation
• Logic-Based Reasoning
• Interpretable Generation
• Hallucination Mitigation