Dynamic Context Tuning for Retrieval-Augmented Generation: Enhancing Multi-Turn Planning and Tool Adaptation

TL;DR

This paper introduces Dynamic Context Tuning (DCT), a lightweight framework that enhances retrieval-augmented generation by supporting multi-turn interactions and evolving toolsets without retraining, improving accuracy and reducing hallucinations.

Contribution

DCT extends RAG with multi-turn dialogue support, dynamic tool selection, and efficient context management, enabling adaptable AI in dynamic domains without retraining.

Findings

Improves plan accuracy by 14%.

Reduces hallucinations by 37%.

Matches GPT-4 performance at lower cost.

Abstract

Retrieval-Augmented Generation (RAG) has significantly advanced large language models (LLMs) by grounding their outputs in external tools and knowledge sources. However, existing RAG systems are typically constrained to static, single-turn interactions with fixed toolsets, making them ill-suited for dynamic domains such as healthcare and smart homes, where user intent, available tools, and contextual factors evolve over time. We present Dynamic Context Tuning (DCT), a lightweight framework that extends RAG to support multi-turn dialogue and evolving tool environments without requiring retraining. DCT integrates an attention-based context cache to track relevant past information, LoRA-based retrieval to dynamically select domain-specific tools, and efficient context compression to maintain inputs within LLM context limits. Experiments on both synthetic and real-world benchmarks show that DCT improves plan accuracy by 14% and reduces hallucinations by 37%, while matching GPT-4 performance at significantly lower cost. Furthermore, DCT generalizes to previously unseen tools, enabling scalable and adaptable AI assistants across a wide range of dynamic environments.