Stream: Nürburgring
Time: 10:00 - 10:45
Db2 utility output is dense, and when you’re chasing down why an object got skipped or what tripped a REORG, it’s tempting to paste it into an LLM and ask. The problem is what comes back. Generic models invent message IDs, thresholds, and extents that don’t exist - fluently, in the exact register of a real Db2 message. On a production system, a confident wrong answer is worse than none, because it costs you nothing to believe it. This session is about grounding: parse the real facts first - real-time statistics, stable message IDs - deterministically, outside the model, then let the AI read only those facts. Every answer cites the exact log line behind it, or it doesn’t get made. No citation, no claim. You’ll leave knowing why generic LLMs fail on Db2 output, how a grounded architecture forces cited answers, and how that cuts troubleshooting time without trusting a hallucination.
There is currently no attachment for Grounding AI on Db2 Diagnostics
I'm working as a Lead Automation Engineer at BMC Software. With a strong background in AI, DevOps and the Automation, I bring a wealth of experience to the table. My expertise lies in designing and implementing automated solutions to enhance efficiency, reduce manual intervention, and ensure the delivery of high-quality software. I thrive on leveraging cutting-edge technologies to streamline workflows and contribute to the success of software development teams. Let's connect and explore how we can drive innovation together! Honored with - IBM Champion 2026, OMP Ambassador 2026, Planet Mainframe Influencer 2026.