Unreliable LLM deployments can create significant business consequences, including damaged brand reputation and operational overhead. To address this, eight advanced prompting and training techniques are presented: Constitutional AI for Principled Decision Making, Advanced RLHF for Reliable Preference Alignment, Synthetic Data Generation for Coverage Gaps, Deploying Adversarial Robustness Training to Resist Manipulation, Chain-of-Thought Prompting for Transparent Reasoning, Strategic Few-Shot Learning for Consistent Performance, Structured System Prompts for Predictable Behavior, and Self-Consistency Checking for Error Detection. These techniques aim to improve model reliability by enhancing consistency, reasoning under pressure, and error detection, ultimately leading to production-ready LLMs that maintain consistent performance across varied deployment scenarios.