Accelerated Innovation

Iteratively Tuning Your GenAI Solutions

Iteratively Tuning Your GenAI Solutions

Workshop
Do you know which parts of your GenAI solution are actually driving performance - and which changes will make things worse?

GenAI systems behave as interconnected components, making improvement non-obvious and progress hard to measure without a disciplined approach. Teams often iterate quickly but lack confidence in whether those iterations are moving the solution forward.

To win, your GenAI solutions must be evaluated holistically, improved deliberately, and governed by clear, data-driven decision criteria.

The Challenge

When iterative tuning is ad hoc or intuition-driven, teams quickly lose signal and confidence:

  • Measuring performance: Assess solution behavior without clear baselines or consistent evaluation signals.
  • Choosing what to fix: Identify many potential improvements but struggle to prioritize the ones that matter most.
  • Managing interdependencies: Change one component without understanding its downstream effects on the system.

Left unchecked, these challenges lead to wasted effort, unstable quality, and stalled GenAI initiatives.

Our Solution

In this hands-on workshop, your team systematically evaluates, prioritizes, and tunes GenAI solutions using structured methods grounded in real performance data.

  • Assess current solution performance using defined signals and evaluation criteria.
  • Map interdependencies across prompts, models, data, and orchestration components.
  • Identify and prioritize improvement opportunities based on impact and risk.
  • Apply targeted tuning actions and observe resulting system-level effects.
  • Establish clear data-driven criteria for go / no-go decisions on further iteration.
Area of Focus
  • Assessing Your Solution's Performance
  • Identifying and Prioritizing
  • Improvement Opportunities
  • Actioning Improvement Opportunities
  • Understanding the Interdependent
  • Nature of GenAI Solutions
  • Making Data-Driven 'Go / No-Go' Decisions
Participants Will
  • Evaluate GenAI solution performance using consistent, repeatable criteria.
  • Identify which components most strongly influence overall solution quality.
  • Prioritize improvement opportunities based on evidence rather than intuition.
  • Apply changes with a clear understanding of system-wide tradeoffs.
  • Make confident go / no-go decisions grounded in observed outcomes.

Who Should Attend:

Technical Product ManagersSolution ArchitectsML EngineersGenAI EngineersEngineering Leads

Solution Essentials

Format

Facilitated workshop (in-person or virtual) 

Duration

2 hours 

Skill Level

Intermediate 

Tools

GenAI solution components, evaluation artifacts, and guided analysis exercises

Ready to replace intuition-driven tuning with data-backed decisions?