The author also discusses the recent news about AWS integrating Amazon Q, their GenAI assistant, into their internal systems and applying it to Java 17 upgrades. While the author acknowledges the operational efficiency of AWS, they express concern about potential operational problems and the loss of opportunities for engineers to discover problematic cases. The author concludes by suggesting a cautious approach towards the adoption of LLMs, warning against being overly enticed by their capabilities.
Key takeaways:
- The author expresses concern over the use of Large Language Models (LLMs) for Root Cause Analysis (RCA) in cloud incidents, fearing it may lead to systemic failures and a decline in developing new experts.
- LLMs may not be able to perform deep root cause identification as human experts do, and may produce superficial results, potentially leading to a lack of progress in enhancing reliability and safety.
- The author also worries about 'automation surprise', where an automated system behaves unexpectedly, causing confusion and potentially dangerous situations.
- Despite these concerns, the author acknowledges the potential of LLMs, but urges caution in their implementation, suggesting that they should not replace human expertise and scrutiny.