Accelerated Innovation

Supporting Your GenAI Solution

GenAI Ops Insights & Continuous Improvement Best Practices

Workshop
Are your GenAI operations generating data—but not actionable insight or measurable improvement?

Many teams collect logs and metrics from GenAI systems but struggle to turn operational data into decisions that improve quality, cost, and reliability. Without structured feedback loops and prioritization, ops insights fail to influence model and data workflows.

To win, your GenAI solutions must continuously learn from operations and translate insight into prioritized improvement.

The Challenge

When GenAI ops insights are underutilized, teams fail to improve system performance over time:

  • Disconnected feedback loops: Operational signals are collected but not fed back into GenAI workflows in a structured way.
  • Shallow root cause analysis: Teams lack observability practices that connect failures and degradation to actionable causes.
  • Unprioritized improvement efforts: Performance fixes are driven by urgency or intuition instead of cost and impact.

These gaps lead to stagnant performance, rising costs, and repeated operational issues.

Our Solution

In this hands-on workshop, your team learns how to turn GenAI operational data into continuous improvement signals.

  • Establish continuous feedback loops within GenAI operations.
  • Use observability data to perform root cause analysis on GenAI failures and degradation.
  • Drive targeted performance improvements using ops-derived insights.
  • Embed operational learnings into model and data workflows.
  • Prioritize improvement initiatives based on cost, impact, and risk.
Area of Focus
  • Establishing Continuous Feedback Loops in GenAI Ops
  • Leveraging Observability for Root Cause Analysis
  • Driving Performance Improvements from Ops Data
  • Embedding Ops Insights into Model and Data Workflows
  • Prioritizing Improvements Based on Cost and Impact
Participants Will
  • Build continuous feedback loops that connect GenAI operations to improvement efforts.
  • Diagnose GenAI issues using structured observability and root cause analysis.
  • Use ops data to guide targeted performance enhancements.
  • Integrate operational insights into ongoing model and data changes.
  • Prioritize GenAI improvements based on measurable cost and impact.

Who Should Attend:

Technical Product ManagersML EngineersPlatform EngineersSite Reliability EngineersEngineering Managers MLOps / LLMOps Engineers

Solution Essentials

Format

Facilitated workshop (in-person or virtual) 

Duration

4 hours 

Skill Level

Intermediate 

Tools

Observability platforms, monitoring data, and GenAI operational tooling in a guided environment

Do you know which GenAI improvements deliver the highest impact for the lowest cost?