Organizations adopting large language models (LLMs) face a challenge in maintaining factual outputs at scale, as non-deterministic models can produce confident yet misleading responses. To address this issue, enterprises need solutions that safeguard truthfulness without slowing development cycles. A multi-pronged approach, including data curation, monitoring strategies, and prompt engineering methods, is necessary to ensure reliability. Real-time monitoring and observability tools, such as Galileo Observe, help track LLM outputs in real time, flagging suspicious behavior, and enabling iterative improvements. Effective guardrails, including tiered thresholds, documentation, and scheduled evaluations, are crucial to detect incorrect or suboptimal LLM outputs early, preventing user exposure. Ongoing iteration is essential, especially as data or domains evolve, to ensure the accuracy of LLM outputs.