Architecting Zero-Hallucination RAG for Enterprise Systems

How to combine dense vector retrieval, sparse keyword indexing, cross-encoder reranking, and citation guardrails for 99.4% factual precision.

By Rajeev Chandran · Aug 2026 · 5 min read

Rajeev Chandran — AI Engineer | FDE | AI Researcher

Key Architectural Insights

Hybrid search catches 35% more relevant niche keywords than dense vector search alone.
Cross-encoder reranking cuts LLM token consumption by 60% while boosting factual precision.
Deterministic citation guardrails prevent ungrounded outputs before rendering to users.

### The Hallucination Problem in Enterprise RAG Naïve RAG systems (vector search + basic LLM prompt) frequently suffer from context drift, irrelevant chunk retrieval, and subtle factual hallucinations. When building for enterprise legal, financial, or healthcare domains, even a 2% error rate is unacceptable. ### The 4-Stage Zero-Hallucination Pipeline 1. **Parent-Document & Semantic Layout Parsing**: Rather than arbitrary 500-token fixed splits, parse documents using layout-aware chunking preserving tables, headers, and bullet relationships. 2. **Hybrid Dense/Sparse Retrieval**: Combine Qdrant dense embeddings with BM25 sparse keyword search to catch technical jargon and exact product IDs. 3. **Cross-Encoder Reranking**: Filter top-50 candidates through Cohere Rerank v3 to score relevance against intent before passing to the model. 4. **Citation Verification Agent**: Run a lightweight secondary evaluation pass verifying that every key claim in the generated text points directly to an exact source paragraph.

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