Effective LLM observability is a systematic approach to gaining visibility into language models' behavior, performance, and outputs through comprehensive monitoring and analysis techniques. It builds on the classic "MELT" framework but expands it to address language models' unique complexities and unpredictable nature. This approach provides visibility into what's actually happening inside complex systems, enabling tracing of each request-response cycle with precision, understanding why a model gave a particular answer, spotting biases, catching security issues, and measuring performance against benchmarks. Key differences between monitoring and observability are essential in this context. Designing LLM systems with observability as a priority demands thoughtful architecture, modular architectures that separate concerns, and creating visibility at each transition point. Modular prompt chains form a key pattern for observable LLM systems, controlled routing layers direct traffic, explicit state tracking is critical, security demands special attention, tools like Galileo support observable architecture implementation, and effective LLM observability balances technical metrics with output quality metrics. Establishing baselines for these metrics is vital, industry-standard frameworks provide standardized evaluation approaches, Galileo simplifies this process, technologies are advancing LLM evaluation beyond traditional metrics, hallucinations in LLMs require sophisticated techniques to detect, architectural patterns like Retrieval-Augmented Generation constrain outputs, measuring hallucination rates systematically requires consistent metrics, statistical pattern analysis can identify characteristic signals of hallucinated content, and effective LLM alerting goes beyond static thresholds. Continuous monitoring is a must for production LLM systems, strategic sampling approaches can reduce data volume while maintaining statistical validity, leading organizations have developed sophisticated approaches to high-volume LLM telemetry, and LLM observability isn't a one-time implementation but a continuous process spanning the entire application lifecycle.