“Eliminating Hallucinations in Enterprise AI Knowledge Bases LLMs are probabilistic word-predictors, not databases. When context is missing, they invent believable but incorrect facts. 4 Anti-Hallucination Guardrails: 1. Hybrid Retrieval (Dense + Sparse): Combine Pinecone / pgvector cosine similarity with traditional full-text search to ensure specific acronyms and part...”
Comprehensive Solution & Technical Breakdown
### Eliminating Hallucinations in Enterprise AI Knowledge Bases
LLMs are probabilistic word-predictors, not databases. When context is missing, they invent believable but incorrect facts.
#### 4 Anti-Hallucination Guardrails: 1. **Hybrid Retrieval (Dense + Sparse):** Combine Pinecone / pgvector cosine similarity with traditional full-text search to ensure specific acronyms and part numbers are never missed. 2. **Reranking:** Pass the top 20 retrieved snippets through a cross-encoder model to sort by exact relevance before passing to the LLM. 3. **Mandatory Citation Prompts:** Force the LLM to cite brackets: `[Doc: EmployeeHandbook.pdf, Page 14]` for every claim made. 4. **Confidence Thresholding:** If similarity score falls below 0.72, return a safe fallback: *"Our documentation does not specify this policy. Would you like to create a support ticket?"*
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