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Evaluating and Improving GenAI Solution Performance

Description

Evaluating and Improving GenAI Solution Performance means consistently assessing how well GenAI models meet user needs, and refining them to increase relevance, reliability, and value. This includes measuring response quality, groundedness, latency, and alignment to business objectives.

Why it's Important

Many GenAI initiatives struggle because performance is poorly defined or inconsistently measured. Without clear metrics and a structured evaluation approach, teams may launch GenAI solutions that produce low-quality or misleading outputs. Organizations that establish strong evaluation capabilities can iterate faster, reduce risks, and better align GenAI functionality with user expectations. Done right, performance insights help improve trust, usability, and competitive advantage.

Why it's Challenging @ Scale

  • Defining What “Good” Looks Like: Many organizations struggle to establish clear, consistent benchmarks for GenAI response quality, groundedness, or efficiency.
  • Fragmented Evaluation Approaches: Evaluation methods often vary across teams or use cases, making it difficult to track progress or compare results.
  • Lack of Real-World Testing: GenAI solutions may be optimized for internal tests but fail in dynamic, real-world user environments.
  • Inconsistent Feedback Loops: Teams lack structured ways to capture and act on user feedback, slowing improvement cycles.
  • Tooling and Data Gaps: Limited access to observability tools, labeled datasets, or real-time monitoring hinders evaluation at scale.

Complexity

High: Improving GenAI solution performance requires technical evaluation capabilities, user input integration, and iteration cycles that can be difficult to coordinate across diverse teams and systems.

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Taking Action

Though most organizations begin their GenAI journey with significant knowledge gaps, there are targeted actions that can be taken to accelerate the process. Select your group’s current maturity, based on your assessment results, and act today.

The most important part of any journey is starting… To move from “Exploring” to “Experimenting”, focus on the following key actions:
  • Explore Key Concepts & Best Practices: Complete the Integrated GenAI Insights Best Practices workshop (2 hrs.) to understand foundational key concepts and explore applied best practices.
  • Understanding integrated insights in GenAI strategies.
  • Identifying insight domains: strategy, product, customer.
  • Mapping KPIs and data sources across functions.
  • Framing use cases and analytical workflows.
  • Planning insight governance and operationalization.
  • Define Your Action Plan: Define a clear, prioritized plan to strengthen Your Integrated Insights, combining the standard launch-pad actions with the targeted activities below.

Jumpstarting Your Plan

  • Define your accountable lead(s), their roles, responsibilities, and committed capacity
  • Deliver your first 90-day quick wins
  • Configure your Delta 7/28 Plan module
  • Define your measures of success and insights plan
  • Build and kick off your change and comms plan

Targeted Activities

  • Complete: Stand up a GenAI Evaluation Metrics Pack (Quality, Safety, Cost, Latency)
  • Complete: Set Up Continuous Evaluation in Production (Monitoring + Drift + Alerts)
  • Complete: Model Monitoring Baseline (Performance, Drift, and Data Health)
  • Deliver Quick Wins: Small, high-impact GenAI projects that can demonstrate tangible value in a short time frame.
  • Identify a narrow, high-traffic use case: Choose a simple GenAI use case with frequent usage to gather performance data quickly.
  • Use baseline performance measures: Establish initial metrics (e.g., response accuracy, latency) to enable visible improvement tracking.
  • Share early outcomes: Package and share results to build awareness and internal momentum.
To move from Experimentation to “Lifting-Off”, prioritize the following actions:
  • Complete one or more of our Deep Dive Courses: Begin exploring key concepts and best practices, including:
  • Nail It Before You Scale It: Assess and optimize your solution or process before adopting it at scale
  • Assess Your Proposed Solution or Process: Define clear evaluation criteria such as precision, latency, hallucination rate, and user satisfaction.
  • Define in-scope Processes and Guardrails: Clarify which workflows are in scope and what boundaries must be maintained (e.g., human-in-the-loop thresholds).
  • Close any Data or Measurement Gaps: Identify missing telemetry, feedback channels, or monitoring dashboards and address them before expanding.
  • Define Your Adoption & Scaling Plan: Create a structured roadmap for how GenAI solutions will be rolled out across teams, workflows, or business units
  • Define Your Phased Implementation Plan: Break rollout into waves aligned to readiness, business value, and model maturity.
  • Build Awareness and Finalize Enablers: Ensure teams have access to evaluation tools, training, and support resources.
  • Operationalize Your Comms Plan: Develop a plan to share performance insights, success stories, and next steps across stakeholders.
To move from Lifting-Off to “Accelerating”, prioritize the following actions:
  • Formalize Your Best Practices: Document and standardize what’s working to ensure consistent, scalable success across teams and use cases
  • Define Standard Evaluation Frameworks: Establish shared methods and KPIs for measuring GenAI quality, groundedness, and efficiency.
  • Publish Reference Use Cases: Highlight evaluated, high-performing GenAI solutions as templates for new efforts.
  • Establish Quality Review Routines: Set regular checkpoints to assess solution performance and flag issues early.
  • Accelerate Your Adoption: Intensify efforts to embed GenAI across your organization by expanding use cases, increasing user engagement, and removing adoption barriers
  • Identify Adjacent Use Cases: Extend proven evaluation methods to related GenAI workflows or user groups.
  • Expand Access to Evaluation Tools: Equip more teams with standardized dashboards, benchmarks, and feedback loops.
  • Remove Friction in Iteration Loops: Reduce cycle times between evaluation, retraining, and relaunch through better tooling and governance.
  • Celebrate Your Wins: Publicly acknowledge team accomplishments to build and sustain adoption momentum
  • Highlight Before-and-After Metrics: Share tangible results that reflect improvement in quality, speed, or user satisfaction.
  • Showcase Team Contributions: Recognize specific teams or individuals who drove GenAI performance breakthroughs.
  • Share Testimonials: Gather internal or external feedback to reinforce the business value of evaluated GenAI solutions.
The “Accelerating” stage represents “Target State” for many capabilities. “Breaking Away”, on the other hand, suggests that the specific Capability represents a clear competitive advantage for your business.
  • Streamline & Embed: Integrate GenAI into core workflows while eliminating friction points to make usage seamless and routine
  • Embed Evaluation into CI/CD Pipelines: Make GenAI quality checks a default part of model deployment workflows.
  • Automate Quality Monitoring: Use real-time performance dashboards and alerts to detect and resolve issues quickly.
  • Standardize Feedback Capture: Build user feedback prompts into solution UIs to continuously fuel evaluation efforts.
  • Leverage Automation: Use GenAI-powered tools and workflows to streamline repetitive tasks, enhance operational efficiency, and reduce manual effort
  • Automate Benchmark Testing: Run recurring performance evaluations using predefined prompts and scenarios.
  • Trigger Retraining Automatically: Link evaluation scores to model retraining or fine-tuning cycles.
  • Integrate Response Scoring APIs: Score GenAI outputs automatically in downstream systems to maintain quality assurance.
  • Evolve & Further Accelerate: Continuously refine GenAI strategies based on insights and outcomes, while expanding into more complex or high-impact use cases
  • Expand Evaluation to Multimodal Use Cases: Extend performance frameworks to include images, video, or voice.
  • Refine Segment-Level Insights: Break down performance by audience, task type, or risk tier to guide optimization.
  • Use Evaluations to Inform Roadmaps: Feed learnings from solution performance into your broader GenAI investment planning.

Key "Watchouts"

As you take action you’ll want to avoid:

  • Over-relying on manual testing: Human spot-checks alone are too slow and inconsistent to support scale.
  • Ignoring user feedback loops: Without structured feedback, teams may miss critical quality issues or improvement opportunities.
  • Focusing only on accuracy metrics: GenAI performance must also include groundedness, latency, and user satisfaction.
  • Delaying evaluation until after deployment: Waiting to assess performance post-launch increases risk and reduces trust.
  • Failing to communicate results: Keeping performance data siloed limits learning and organizational momentum.

Targeted Benefits

While Evaluating and Improving GenAI Solution Performance can be challenging, its benefits are clear and compelling, including:

  • Improved User Trust and Satisfaction: Clear evaluation frameworks build credibility and confidence in GenAI outputs.
  • Faster Iteration Cycles: Structured evaluation allows teams to identify issues quickly and improve continuously.
  • Higher Quality Solutions: Performance monitoring ensures GenAI outputs meet expectations across varied use cases.
  • Cross-Team Alignment: Shared benchmarks help align business, product, and technical teams around what success looks like.
  • Competitive Advantage: Consistently optimized GenAI solutions differentiate your organization in the market.

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Eddie
Accelerated Innovation

Hi, I'm Eddie 👋

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