As engineers, we're constantly juggling priorities; resolving production issues, optimizing performance, and ensuring systems remain resilient under pressure. The complexity of modern applications and their sprawling infrastructures often means spending hours sifting through logs, writing custom queries, and piecing together scattered insights. This is where New Relic AI has truly made a difference. By leveraging large language models (LLMs), it allows me to interact with system data in a way that feels more natural. Instead of wrangling with raw data or creating complex queries, I can ask questions in plain English and get meaningful, actionable insights right away. It's a game-changer for simplifying workflows and helping me focus on what truly matters.

In this blog post, I'll share four ways New Relic AI has made my engineering work easier, faster, and less stressful. Whether it's identifying hidden issues, automating tedious tasks, or streamlining monitoring, you'll see just how this tool has transformed my approach to observability.

Debugging can be one of the most time-consuming tasks for an engineer, especially when dealing with large, distributed systems. When something breaks, your first instinct is to dive into logs, but that often means wading through hundreds or thousands of lines of text to figure out what went wrong. New Relic AI changes the game by making log analysis not just faster, but smarter. Instead of manually searching through logs, you can ask New Relic AI questions in plain language, and it will summarize errors, highlight patterns, and even suggest next steps.

Imagine you're working on the Ad service during a high-traffic event, and some transactions start failing intermittently. Instead of jumping into multiple log files, you query New Relic AI with something like:

New Relic AI instantly generates a query in New Relic Query Language (NRQL) and provides a high-level count of failing transactions, as shown below: