Enabling GenAI Support Team Members
Once GenAI systems reach production, the real challenge becomes sustaining quality, reliability, and trust as models, data, and usage patterns continuously change.
To win, your GenAI solutions need strong operational foundations that enable fast triage, reliable performance, and continuous improvement.
Without a disciplined approach to supporting GenAI in production, teams struggle to:
Detect and diagnose issues early - Quality drops, hallucinations increase, and failures surface only after users complain.
Coordinate across operational domains - LLM behavior, data changes, access controls, and reliability issues are handled in silos.
Improve systematically - Teams lack clear insights to guide prioritization, tuning, and iteration.
These gaps increase operational risk, reduce user trust, and make GenAI systems expensive and difficult to sustain.
An integrated, hands-on offering focused on building the practical skills GenAI support teams need to operate GenAI solutions reliably in production. Participants will:
Establish a disciplined, scalable support approach using Assessments, Acceleration Guides, and an Integrated Insights foundation.
Explore real-world operational challenges grounded in what actually happens when GenAI systems run at scale.
Observe proven practices, templates, and examples in guided sessions and curated materials.
Apply these methods through structured exercises and hands-on coaching.
LLM Ops – Manage model behavior, performance, versioning, and cost as models evolve in production.
Data Ops – Ensure data quality, freshness, and consistency that directly impact GenAI outputs.
Identity, Access Management, and Change Management – Control access, manage changes safely, and reduce unintended downstream impacts.
Monitoring and Alerting – Detect quality, performance, and reliability issues early using GenAI-specific signals.
Reliability – Design for resilience, graceful degradation, and predictable behavior under real-world conditions.
Insights and Continuous Improvement – Turn operational data into prioritized actions that drive measurable improvement.
Stronger Production Stability – Reduce outages, regressions, and unexpected behavior in live GenAI systems.
Faster Issue Detection and Triage – Identify root causes across models, data, and orchestration layers more quickly.
Improved Quality and Trust – Sustain reliable, high-quality GenAI experiences for users over time.
Clear Operational Ownership – Align teams around roles, responsibilities, and decision paths for GenAI support.
Continuous Performance Improvement – Use insights and evaluation signals to guide ongoing optimization instead of reactive fixes.