RAG 2.0: How GraphRAG, CoVe, and RL Slash AI Hallucinations by 20% in Real-World Applications

Author(s): R. Thompson (PhD)

Originally published on Towards AI.

Large Language Models (LLMs) power modern applications across domains — from healthcare and legal to e-commerce and education. Despite their versatility, LLMs suffer from hallucinations: responses that sound accurate but lack factual grounding. Retrieval-Augmented Generation (RAG) emerged as a method to mitigate this by providing external context. Yet, basic RAG is insufficient in dynamic, noisy, or critical settings.

This article presents advanced retrieval techniques that evolve RAG into a more precise, context-aware, and real-world-ready solution. Alongside code examples and case applications, it highlights:

• State-of-the-art retrieval strategies like GraphRAG and CoVe • Verified metrics for hallucination reduction • A use case on uplift modeling in e-commerce • Challenges and modern solutions for scalable deployment

Hallucinations result from gaps in training data or reliance on statistical likelihoods. A 2023 study in Journal of Medical Internet Research confirmed:

• 19% of AI-generated medical suggestions were factually wrong without grounding • In marketing, misclassified customers lead to 30% budget waste (Forbes, 2022) • Legal and compliance applications report up to 25% error rate without retrieval layers

Basic RAG helps, but fails in:

• High-noise retrieval scenarios • Complex, multi-hop queries • Dynamic user behavior changes

• Leverages JSON, tabular datasets, and knowledge graphs • Reduces hallucinated steps from 21% to… Read the full blog for free on Medium.

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Published via Towards AI