Hallucination Mitigation in Enterprise LLM Apps

Problem

Hallucinations — confident but factually incorrect outputs — represent one of the most critical risks in deploying LLMs for enterprise use cases. This system provides a comprehensive framework for evaluating, benchmarking, and mitigating hallucinations across multiple strategies, from simple prompt engineering to full retrieval-augmented generation pipelines with multi-stage validation.

Architecture Overview

The system implements a layered architecture with four key stages:

1. Guardrails Layer — prompt templates, instruction constraints, and system role enforcement to shape LLM behavior before generation
2. Retrieval Layer — four RAG variants (naive, hybrid BM25+dense, reranking, multi-query) providing grounding context
3. LLM Orchestrator — prompt building and context assembly for the language model
4. Validation Layer — schema validation, citation enforcement, self-critique, and multi-step validation pipeline for output verification

Mitigation Strategies

The system evaluates nine strategies from no mitigation (baseline) through full pipeline deployment:

• Grounded Prompting (~20-35% hallucination reduction)
• Naive RAG (~40-55% reduction)
• Hybrid RAG (BM25 + dense retrieval, ~50-65% reduction)
• Reranking RAG (~55-68% reduction)
• Citation Enforcement (~65-78% reduction)
• Full Pipeline (all strategies combined, ~70-85% reduction)

Technical Decisions

• Modular evaluation harness — strategies can be individually toggled and combined, enabling controlled A/B comparison
• Five benchmark datasets — factual, ambiguous, missing-knowledge, adversarial, and long-context queries test different failure modes
• Mock mode — full evaluation pipeline runs without API keys for development and CI, using deterministic mock responses

Tech Stack

• Backend: Python
• ML/NLP: RAG pipelines, embedding models, rerankers
• Testing: pytest
• Evaluation: Custom harness with hallucination rate, citation accuracy, latency, and refusal rate metrics