Hallucinations in LLMs: A Deep Technical Dive into Causes, Detection, and Mitigation

2026-02-13 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

Large language models (LLMs) like GPT, LLaMA, and Claude frequently produce "hallucinations," which are confident, plausible-sounding responses that are factually incorrect or unsupported by evidence. These can be intrinsic, contradicting provided context, or extrinsic, inventing unsupported information. Hallucinations are inherent to LLM training, which optimizes for next-token prediction rather than factual accuracy, storing knowledge associatively rather than precisely. Decoding choices, such as greedy or top-k/top-p sampling, can amplify uncertainty into confident lies by removing "I don't know" options. Other causes include exposure bias, error compounding, instruction tuning rewarding helpfulness over truthfulness, and attention failures within long context windows. Detecting hallucinations is challenging, relying on human evaluation, automatic metrics like FactScore or NLI, and engineering methods such as self-consistency sampling, confidence scores, retrieval verification, and citation grounding.

Key takeaway

For AI Engineers deploying LLMs in production, you should prioritize building systems that actively detect and mitigate hallucinations rather than assuming model correctness. Implement Retrieval-Augmented Generation (RAG) with strict grounding, utilize constrained decoding for critical outputs, and integrate post-generation verification steps. Your pipeline should be designed to fail safely by refusing to answer when evidence is weak, ensuring reliability and trustworthiness in factual applications.

Key insights

LLM hallucinations are inherent to their probabilistic nature, driven by training objectives that prioritize fluency over factual accuracy.

Principles

• LLMs are statistical compressors, not databases.
• Decoding choices amplify internal uncertainty.
• Helpfulness can conflict with truthfulness.

Method

A low-hallucination pipeline involves input classification, retrieval of relevant sources, grounded generation with strict prompts, claim verification, and output safety mechanisms like disclaimers or refusals.

In practice

• Implement RAG with strict "answer only from context" policies.
• Use constrained decoding for structured outputs.
• Employ post-generation verification systems.

Topics

• LLM Hallucinations
• Retrieval-Augmented Generation
• Hallucination Mitigation
• Probabilistic Decoding
• Model Evaluation

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

Related on AIssential

• 5 Practical Techniques to Detect and Mitigate LLM Hallucinations Beyond Prompt Engineering — LLM hallucinations are a system problem requiring multi-layered detection and mitigation beyond prompt engineering.
• Extrinsic Hallucinations in LLMs — LLM hallucinations arise from pre-training data issues and fine-tuning new knowledge, necessitating robust detection and mitigation strategies.
• Why AI Makes Things Up: Understanding Hallucinations in Language Models — LLM hallucinations are a structural outcome of probabilistic training, not a fixable bug, requiring systemic mitigation.
• Inside RAG: How Your Question Becomes an Answer — RAG enhances LLM responses by retrieving relevant data before generation, preventing hallucination.
• Evidence Graph Consistency in Retrieval-Augmented Generation: A Model-Dependent Analysis of Hallucination Detection — Graph-based RAG hallucination detection is model-dependent, reversing for GPT-class models due to differing hallucination styles.
• Hallucinations in LLMs Are Not a Bug in the Data — LLM hallucination is an active suppression of factual accuracy in favor of contextual coherence, not a retrieval failure.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.