Java Flight Recorder (JFR) provides a detailed view of what is happening inside a Java application. It captures information about garbage collection, threads, heap activity, method execution, lock contention, I/O, and other JVM events. But while JFR provides a wealth of diagnostic data, turning that data into a clear root cause can still require significant time and expertise.
In our latest yCrash webinar, “JFR ❤️ AI: Ultimate Combo for Troubleshooting,” Ram Lakshmanan, Founder of yCrash and a Java performance and troubleshooting expert, demonstrated how combining Java Flight Recorder with AI can make this process more efficient.
The session explored how semantic parsing can transform raw JFR events into structured data that an LLM can reason over, while deterministic processing and carefully designed prompts can help produce more reliable troubleshooting insights.
Attendees also saw how this approach can be applied to real-world production JFR recordings and learned a practical blueprint for combining JFR and LLMs in their own troubleshooting workflows.
Why JFR and AI Matter for Java Troubleshooting
JFR can capture thousands of events during the execution of a Java application. The challenge is rarely a lack of data. Instead, engineers often need to correlate different signals across memory, garbage collection, threads, CPU activity, locks, and I/O to understand what is actually causing a performance problem.
Manually performing this correlation can be time-consuming, particularly when investigating large production recordings.
AI can potentially help engineers reason across these different signals, but simply providing raw JFR data to an LLM does not necessarily produce reliable results. Large amounts of diagnostic data need to be structured and interpreted carefully before AI can provide useful conclusions.
The webinar demonstrated an approach that combines semantic parsing, structured diagnostic data, and deterministic prompting to make JFR analysis more suitable for AI-assisted troubleshooting.
Key Takeaways from the Session
- Understand how semantic parsing transforms raw JFR events into structured, LLM-ready data.
- Learn how AI can correlate different JFR signals to help identify Java performance root causes.
- See how deterministic prompting can make AI-assisted troubleshooting more reliable and reduce hallucinations.
- Explore a practical workflow using real-world production JFR recordings.
- Learn how to combine JFR and LLMs as part of a Java performance troubleshooting workflow.
- Get expert insights from Ram Lakshmanan during the live Q&A session.
Slide Deck
Revisit the concepts and workflow discussed during the webinar with the complete slide deck.
The presentation covers JFR data, semantic parsing, structured diagnostic information, AI-assisted analysis, deterministic prompting, and the practical workflow demonstrated during the session.
Webinar Recording
Watch the complete webinar recording to see how JFR data can be transformed into structured information that an LLM can reason over.
The session includes a practical demonstration using production JFR captures and walks through the parsing and prompting techniques used to turn complex JFR data into actionable troubleshooting insights.
[Webinar Recording – Coming Soon]
Q&A Session
The webinar concluded with an interactive Q&A session where Ram Lakshmanan answered questions from attendees about combining JFR with AI for Java performance troubleshooting.
The discussion provided additional perspectives on applying the approach to real-world JVM investigations, including how engineers can structure diagnostic data and use AI more reliably when analyzing complex performance problems.
[Q&A Recording – Coming Soon]
Stay Tuned for Upcoming Webinars
Our monthly webinar series brings together Java performance experts and practitioners to explore practical techniques for JVM troubleshooting, performance engineering, observability, and emerging technologies.
Stay tuned for upcoming sessions covering real-world Java performance challenges and approaches to making production troubleshooting faster and more effective.
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