INTENT-GUIDED RETRIEVAL-AUGMENTED GENERATION WITH LORA-BASED INTENT ROUTING FOR INTELLIGENT CUSTOMER SUPPORT

Abstract

Automated customer support technologies based on large-scale language models (LLMs) suffer from three classic failure modes: false creation of non-existing policies, inconsistent answers to the same questions, and misleading interpretations of user intentions due to confusing, joking, or vague inquiries. Large-scale language model (LLM)-based automated customer support technologies fail due to false policy creation, inconsistent answers to the same questions, and misleading interpretations of user intentions due to confusing, joking, or vague enquiries. Instant-Guided Retrieval-Augmented Generation with LoRA-based Intent Routing, a novel customer assistance system with intent-based retrieval, was successfully created. The Baseline pre-trained Llama-3-8B-Instruct model with no external grounding, a Naive Retrieval-Augmented Generation pipeline (Solution V1) that embeds SOP passages with sentence-transformers/all-MiniLM-L6-v2, indexes them in ChromaDB, and augments the prompt with the top-retrieved passage at inference time, and a Hybrid, The same externally held test partition was striated and an adversarial partition was screened for hedging and frustration language to evaluate all three procedures. Evaluations used ROUGE-1, ROUGE-L, BLEU, Format adherence rate, exact match, and fuzzy match accuracy. Based on 50 samples, retrieval increased ROUGE-1 from 0.2116 to 0.277, a 31.4% increase over the ungrounded benchmark, and BLEU from 0.0030 to 0.0328, an almost tenfold gain. With a fine-tuned intent router added to retrieval, ROUGE-1 would rise 3.9% to 0.2887 and BLEU to 0.0478 (+45.4% over V1). For the entire and adversarial sets, the intent router adheres to JSON Format 100% with 25-36% exact match and 42-46% fuzzy match accuracy.