Addressing hallucinations in Large Language Models (LLMs) is crucial for building reliable AI applications, as these inaccuracies can undermine user trust and introduce risks. This guide offers a comprehensive approach to mitigate hallucinations, starting with optimizing prompts through strategic engineering to provide clearer instructions and reduce the model's creative freedom. It advocates for implementing Retrieval-Augmented Generation (RAG) to ground responses in factual sources and suggests using tools like Helicone for monitoring and evaluating hallucination rates. Additional techniques include user feedback collection, scoring outputs, and setting up automated evaluators to track hallucinations. Advanced strategies involve fine-tuning with high-quality data, employing rule-based guardrails for factual accuracy, and combining RAG with fine-tuning for optimal results. The guide emphasizes that reducing hallucinations is an ongoing, multi-faceted process that requires continuous monitoring and adaptation to evolving application needs.