Mitigating Hallucination Ratios in RAG Systems Using Re-Ranking and Groundedness Checks

The Challenge of RAG Hallucination§

Retrieval-Augmented Generation (RAG) is designed to ground Large Language Models in domain-specific documents. However, naive RAG setups—using simple cosine similarity over vector embeddings—frequently retrieve irrelevant context chunks. When fed noisy context, the model either invents plausible-sounding facts (hallucinations) or ignores relevant information.

To achieve 99%+ answer accuracy in enterprise search applications, engineering teams must implement Hybrid Retrieval, Cross-Encoder Re-Ranking, and Groundedness Verification.

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The 3-Step High-Precision RAG Architecture§

[ User Query ] ➔ [ Hybrid Search: BM25 + Vector ] 
               ➔ [ RRF Fusion ] 
               ➔ [ Cross-Encoder Re-Ranker ] 
               ➔ [ Top 3 Context Chunks ] 
               ➔ [ Grounded Generator ] 
               ➔ [ Attribution Checker ] ➔ [ Verified Answer ]

1. Hybrid Search with Reciprocal Rank Fusion (RRF)§

Combines sparse keyword matching (BM25) with dense semantic embeddings. RRF merges rank lists by balancing sparse and dense scores.

2. Cross-Encoder Re-Ranking§

Passes the top 20 candidate chunks through a Cross-Encoder model (e.g., bge-reranker-large). Unlike dual-encoders, Cross-Encoders evaluate full joint attention between query and document text, filtering out false positive matches.

3. Post-Generation Groundedness Verification§

A lightweight verification model verifies that every sentence in the generated output maps directly to a citation quote from the retrieved context. If a sentence lacks grounded source attribution, it is automatically pruned.