What We Learned from a Year of Building with LLMs (Part I)

The article discusses the importance of robust guardrails in large language models (LLMs) to detect and filter out undesired output, such as hate speech or personally identifiable information (PII). The authors highlight that while prompt engineering can help, it's not enough, and guardrails are needed to ensure accuracy and relevance. They also emphasize the need for logging inputs and outputs for debugging and monitoring. Additionally, the article touches on the issue of hallucinations in LLMs, where the model produces output that is not based on the input context. The authors suggest combining prompt engineering and factual inconsistency guardrails to address this issue. Overall, the article emphasizes the importance of careful design and implementation of LLM systems to ensure they are safe, accurate, and relevant.Note: The text is a collection of lessons learned from working with Large Language Models (LLMs) and is written by multiple authors who have experience in machine learning engineering, AI research, and data science.