LLM Debugging: Analyze Logs and Fix Chatbot Hallucinations

When a production chatbot starts generating incorrect or nonsensical answers, finding the root cause can feel like searching for a needle in a haystack. This course teaches you how to systematically inspect system logs to diagnose and resolve Large Language Model (LLM) hallucinations. You will transition from guessing why an AI model failed to confidently pinpointing errors in prompts, retrieval steps, and model outputs. You will learn how to read structured logs, evaluate retrieval-augmented generation pipelines, and apply targeted fixes to restore chatbot accuracy. What you'll learn: Understand the foundational causes of LLM hallucinations and how they manifest in production systems; Analyze structured application logs to trace user queries, system prompts, and model responses; Evaluate retrieval-augmented generation (RAG) steps to identify data fetching and context window issues; Apply modern prompt engineering adjustments and system instructions to minimize unexpected outputs; Configure basic LLM observability patterns to monitor system behavior and catch errors early; Practice debugging scenarios through written step-by-step log analysis exercises. The course begins with essential terminology, outlining the core mechanics of LLMs and why hallucinations occur. From there, you will read through realistic log files, dissecting errors step-by-step, and exploring practical mitigation strategies to keep your AI applications reliable. Designed for beginner developers, technical support specialists, and AI enthusiasts, this course requires no advanced machine learning background. Start reading today to master the essential skills of LLM troubleshooting and keep your production chatbots on track.