How to Improve LLM Safety and Reliability

Safety and reliability are crucial aspects of Language Models (LLMs) as they become increasingly integrated into customer-facing applications. Real-world incidents highlight the need for robust safety measures in LLMs to protect users, uphold brand trust, and prevent reputational damage. Evaluation needs to be tailored to specific tasks rather than relying solely on benchmarks. To improve safety and reliability, developers should create evaluators, use experiments to track performance over time, set up guardrails to protect against bad behavior in production, and curate data for continuous improvement. Tools like Phoenix can help navigate the development lifecycle and ensure better AI applications.