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Adipta Martulandi
Adipta Martulandi

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A Practical Guide to Identifying and Mitigating Hallucinations Outputs in Language Models

Language models have revolutionized how we interact with AI, enabling powerful applications in content creation, customer support, education, and more. However, these models are not without their challenges. One of the most critical issues is hallucination — when a language model confidently produces false or misleading information. These hallucinations can undermine trust, mislead users, and even cause significant harm in sensitive applications.

In this guide, we’ll dive into the concept of hallucination in language models, exploring why it happens and its potential impact. More importantly, we’ll introduce DeepEval, a robust library in Python designed to evaluate and manage the outputs of language models effectively. You’ll learn how to use DeepEval’s metrics to detect, analyze, and mitigate hallucination in real-world scenarios.

Whether you’re a developer, researcher, or enthusiast working with language models, this guide will equip you with the tools and strategies to ensure your AI outputs remain accurate, reliable, and trustworthy. Let’s get started!

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Image of Timescale

Timescale – the developer's data platform for modern apps, built on PostgreSQL

Timescale Cloud is PostgreSQL optimized for speed, scale, and performance. Over 3 million IoT, AI, crypto, and dev tool apps are powered by Timescale. Try it free today! No credit card required.

Try free

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