Managing AI Hallucinations and Bias in Large Language Models

Large Language Models are powerful tools, but their tendency to generate convincing yet false information (hallucinations) poses a significant challenge for reliable application. This course provides a foundational understanding of why LLMs produce unreliable outputs, covering data bias, architectural limitations, and context drift. You will gain practical skills to identify, mitigate, and reduce these inaccuracies, enabling you to deploy AI systems with greater confidence and ethical awareness. What you'll learn: * Understand the core mechanisms that lead to AI hallucinations and data-driven bias in generative models. * Analyze the impact of training data quality and context window limitations on model reliability. * Apply effective prompt engineering techniques and grounding methods, such as Retrieval-Augmented Generation (RAG), to improve output accuracy. * Practice systematic verification and validation workflows to detect and correct unreliable AI outputs. * Configure basic guardrails and ethical guidelines for responsible deployment of LLMs in production environments. The material begins with essential terminology and the philosophical context of AI reliability, before moving into practical methods for diagnosing and preventing common failure modes. We conclude by exploring modern approaches to output verification. This course is designed for beginners—developers, product managers, and technical users—who are starting to work with Large Language Models and need to ensure their outputs are trustworthy. No prior machine learning expertise is required. Start reading today and learn how to build more reliable AI systems.