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AI Research

Hallucinations in AI: Understanding and Detecting Them

Large Language Models (LLMs) have demonstrated a remarkable ability to generate fluent, coherent, and human-like text. However, beneath this polished exterior lies a significant challenge: Hallucination. This phenomenon, where an LLM generates information that is nonsensical, factually incorrect, or unfaithful to a provided source, is one of the most critical hurdles to their reliable deployment.

October 2025 Read More

AI Research

Beyond “Vibe Checks”: A Practical Guide to Understand Evaluation of Single-Agent Systems

Everyone is building AI agents, but there's a big gap between a cool demo and a reliable, production-ready system. Fix one issue, and something else breaks sometimes badly. This blog breaks down how agents work, why evaluating them is essential, and the key evaluation methods needed to move beyond simple “vibe checks.”

November 2025 Read More

LLM as a Judge Research

Creating Toxicity Detection Using LLM-as-a-Judge: A Guide and Best Practices

With the rise of large language models, there's a growing trend to use them as judges, not just generators. This blog explores LLM-as-a-Judge from first principles, examining its use across evaluation tasks, diving deep into toxicity detection, and applying best practices to create more reliable and robust evaluation systems.

January 2026 Read More

GenGuardX Blog

Hallucinations in AI: Understanding and Detecting Them

Beyond “Vibe Checks”: A Practical Guide to Understand Evaluation of Single-Agent Systems

Creating Toxicity Detection Using LLM-as-a-Judge: A Guide and Best Practices