Mitigating LLM Hallucinations: A Comprehensive Review of Techniques and Architectures

This paper presents a systematic review of current guardrail technologies designed to detect and mitigate hallucinations in large language models (LLMs), analyzing their effectiveness across 15 application domains. This paper furthe analyzes contemporary approaches for detecting and mitigating hallucinations in large language models (LLMs), comparing their effectiveness across enterprise use cases. Hallucinations—instances where models generate plausible but factually incorrect or nonsensical content—pose critical challenges for production LLM deployments, with industry reports indicating financial losses exceeding $250M annually from hallucination-related incidents. We categorize contemporary approaches into detection-based, prevention-based, and correction-based methods, evaluating their performance across three key dimensions: accuracy improvement (15-82% reduction in hallucinations), computational overhead (5-300ms latency impact), and implementation complexity. Our meta-analysis reveals that while hybrid retrieval-augmented generation (RAG) architectures show consistent 35-60% error reduction, emerging neurosymbolic techniques such as automated reasoning checks and multi-agent validation systems demonstrate superior performance in high-stakes domains. We further review recent developments evaluation framework for standardized comparison of hallucination correction models. The review identifies seven critical research gaps, including theoretical inconsistencies, real-time performance limitations, and evaluation challenges, while highlighting innovations from 28 industry leaders, including Amazon Bedrock's contextual grounding, NVIDIA NeMo's open-source toolkit, and Guardrails AI's provenance validation. Our findings underscore the need for balanced solutions that address the trade-off triangle of accuracy, latency, and cost to enable reliable LLM deployments in enterprise settings.