Accelerated Innovation

Closing Your GenAI Model Capability Gaps

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Closing Your GenAI Model Capability Gaps

Description

Closing Your GenAI Model Capability Gaps is the process of identifying and addressing limitations in model performance that affect your GenAI solution’s accuracy, relevance, and reliability. It involves tuning models for your specific business context, resolving known issues, and systematically enhancing model outputs based on real-world usage and feedback.

Why it's Important

As organizations scale GenAI solutions, early performance limitations-such as hallucinations, gaps in domain expertise, or inconsistent behavior-can erode user trust and stall adoption. Closing these gaps is essential to move beyond MVPs and toward high-performing, production-ready solutions. By continuously optimizing model capabilities, teams can improve output quality, unlock new use cases, and extend GenAI impact across the business. It also enables differentiated performance in areas where generic foundation models fall short, supporting competitive advantage through customization and refinement.

Why it's Challenging @ Scale

  • Model weaknesses vary by use case: Performance issues are often unique to each domain, making it hard to apply a single fix across solutions.
  • Poor observability into model behavior: It’s difficult to trace GenAI output issues back to specific root causes without robust evaluation frameworks.
  • Tradeoffs across quality dimensions: Fixes to one issue (e.g., hallucination) may degrade performance elsewhere (e.g., creativity or fluency).
  • Time-consuming tuning and testing: Iterating on model performance often requires deep technical expertise and manual experimentation.
  • Lack of automated measurement: Many organizations lack tooling to continuously monitor, benchmark, and validate GenAI model performance.

Complexity

High: Maturing this capability requires cross-functional effort to evaluate, prioritize, and address model capability gaps while balancing tradeoffs and minimizing disruption to broader solution performance.

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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 Iteratively Tuning Your GenAI Solutions workshop (2 hrs.) to understand foundational key concepts and explore applied best practices
  • 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.
  • Define Your Action Plan: Outline concrete, prioritized steps your organization will take to implement GenAI Strategy.
  • Align on your Current State and define your Target State.
  • Create an actionable enablement plan.
  • Define target timeline and measures of success.
  • Deliver Quick Wins: Small, high-impact GenAI projects that can demonstrate tangible value in a short time frame.
  • Launch a Domain-Specific Model Calibration Test: Evaluate current model outputs against expert-defined benchmarks to identify key performance gaps.
  • Apply Prompt Engineering Fixes for Common Errors: Use targeted prompt tuning to mitigate hallucinations, bias, or irrelevant outputs.
  • Stand Up a Model Capability Issue Tracker: Create a shared backlog to capture, categorize, and prioritize model-related improvement opportunities.
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:
  • Optimizing Your Data.
  • Optimizing Your Model(s).
  • Optimizing Your Natural Language Understanding & Intent Classification.
  • Optimizing Your GenAI Search.
  • Optimizing Your GenAI Retrieval.
  • Optimizing Your GenAI Responses.
  • Optimizing Your Safeguards.
  • Optimizing Your GenAI Solution Costs.
  • Optimizing Your GenAI Support.
  • Optimizing Your EDD Approach.
  • Nail It Before You Scale It: Assess and optimize your solution or process before adopting it at scale
  • Assess Your Proposed Solution or Process: Review current model performance across known error types and assess root cause categories.
  • Define in-scope Processes and Guardrails: Establish clear thresholds and constraints for model behavior based on risk, context, and use case.
  • Close any Data or Measurement Gaps: Identify missing telemetry, labels, or ground-truth data needed to evaluate and tune model quality.
  • 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: Map roll-out strategy for model capability improvements across prioritized journeys.
  • Build Awareness and Finalize Enablers: Provide toolkits, examples, and measurement frameworks to delivery teams to guide model optimization.
  • Operationalize Your Comms Plan: Communicate key performance improvements and upcoming changes to affected 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
  • Publish Model Tuning Guidelines: Document successful tuning patterns and fixes for common capability gaps.
  • Create Model Evaluation Templates: Provide standardized formats for teams to assess and report model performance across domains.
  • Embed Model Quality Reviews into Workflows: Integrate evaluation checkpoints into development and release processes.
  • Accelerate Your Adoption: Intensifying efforts to embed GenAI across your organization by expanding use cases, increasing user engagement, and removing adoption barriers
  • Expand Model Optimization Coverage: Ensure tuning and performance improvements extend across core GenAI use cases.
  • Provide Training on Model Capability Diagnosis: Equip teams to recognize and respond to specific model behavior issues.
  • Launch a GenAI Improvement Hub: Centralize tooling, feedback loops, and best practices to coordinate model optimization efforts.
  • Celebrate Your Wins: Publicly acknowledge team accomplishments to build and sustain adoption momentum
  • Highlight Model Performance Gains: Share examples of improvements in accuracy, relevance, or stability.
  • Share Stories of Resolved Issues: Show how fixing model gaps led to better user experience or reduced risk.
  • Recognize Key Contributors: Call out individuals and teams driving meaningful model improvements.
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 Model Diagnostics into Authoring Tools: Enable teams to test and evaluate model behavior directly within solution development environments.
  • Provide Real-Time Error Detection: Equip co-pilots or toolchains to flag potential model issues during prompt design or output review.
  • Standardize Model Fix Workflows Across Teams: Create cross-functional playbooks for diagnosing and resolving model issues at scale.
  • Leverage Automation: Using GenAI-powered tools and workflows to streamline repetitive tasks, enhance operational efficiency, and reduce manual effort
  • Automate Model Capability Scoring: Use AI to continuously evaluate solution outputs against benchmark criteria.
  • Generate Fix Suggestions Automatically: Recommend changes to prompts, data, or configuration based on performance history.
  • Fine-Tune Using Closed-Loop Learning: Train models on in-domain usage data and post-production feedback to improve future outputs.
  • Evolve & Further Accelerate: Continuously refining GenAI strategies based on insights and outcomes, while expanding into more complex or high-impact use cases
  • Update Capability Gap Libraries Based on Emerging Issues: Continuously refine your understanding of where and why model issues arise.
  • Extend Optimization Practices to Multimodal Models: Apply capability gap closure approaches to image, voice, or video use cases.
  • Benchmark Model Quality vs. Industry Standards: Use comparative evaluation to measure performance gains against market norms.

Key "Watchouts"

As you take action you’ll want to avoid:

  • Overcorrecting with overly restrictive tuning: Excessive prompt or model constraints can degrade flexibility and usefulness
  • Relying on intuition instead of data: Skipping measurement frameworks leads to wasted effort and unclear results
  • Treating all model issues the same: Hallucination, bias, and coverage gaps require different strategies and solutions
  • Ignoring user-reported problems: Valuable performance signals are often embedded in frontline feedback
  • Letting model quality plateau: Continuous improvement is essential to keep pace with user expectations and use case complexity

Targeted Benefits

While Closing Your GenAI Model Capability Gaps can be challenging, its benefits are clear and compelling, including:

  • Higher-quality GenAI outputs: Targeted tuning reduces hallucination, improves accuracy, and increases business relevance
  • Faster time-to-value: Fixing known issues accelerates solution adoption and impact
  • Stronger user trust: Reliable outputs build confidence in GenAI tools and recommendations
  • Lower operating risk: Addressing critical model weaknesses reduces the chance of harmful or off-brand responses
  • Clear competitive advantage: Stronger model performance enables differentiated solutions and experiences

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Eddie
Accelerated Innovation

Hi, I'm Eddie 👋

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