Debugging large language model (LLM) applications presents unique challenges compared to traditional software due to their non-deterministic behavior and complex multi-step workflows, which can lead to distinctive failure modes such as hallucinations and prompt injection attacks. Effective debugging is crucial for maintaining user satisfaction and involves comprehensive logging, multi-step workflow tracing, and employing observability tools like Helicone to monitor input prompts, generated responses, token usage, and latency. Advanced techniques such as session replay, A/B testing of prompts, and automated LLM evaluations are essential for identifying and resolving issues, improving output quality, and ensuring security. As LLM applications evolve, ongoing adaptation of debugging strategies and investment in robust observability infrastructure are necessary to create reliable, cost-effective, and user-friendly AI applications.