Demystifying LLMs: An Introduction to Model Interpretability

Large language models often feel like black boxes, making it difficult to trust their outputs or debug unexpected behavior. Understanding how these models arrive at their decisions is crucial for building responsible, reliable, and safe AI systems. In this text-based course, you will transition from treating models as mysterious entities to confidently auditing their reasoning and diagnosing biases. You will learn the core methodologies used to inspect, explain, and evaluate model outputs. What you'll learn: 1. Understand foundational concepts of model interpretability, transparency, and explainability. 2. Analyze attention mechanisms to see which parts of an input prompt influence the model's output. 3. Evaluate reasoning faithfulness and identify common causes of model hallucination. 4. Apply basic probing techniques to inspect internal representations and hidden layers. 5. Assess modern evaluation frameworks, including interpretability in retrieval-augmented generation (RAG) systems. 6. Debug unexpected AI behaviors and mitigate biases using structured auditing workflows. You will start with essential terminology and the historical context of model explainability before progressing to step-by-step written analyses of model weights, activation tracking, and modern evaluation strategies. This course is designed for aspiring AI developers, data analysts, and tech enthusiasts who want to understand the inner workings of modern language models, with no advanced mathematical background or prior machine learning experience required. Start reading today to unlock the black box of artificial intelligence and build more transparent AI solutions.