Accelerated Innovation

Shortlisting High-Potential LLMs for Evaluation

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Shortlisting High-Potential LLMs for Evaluation

Description

This capability focuses on narrowing down the list of available LLMs to a manageable set of candidates that are most likely to meet your use case needs. It involves aligning model selection to business goals, technical requirements, and evaluation readiness.

Why it's Important

With dozens of LLMs available and new entrants emerging regularly, it’s easy to get overwhelmed by options. Shortlisting allows teams to focus time and resources on the models that offer the best fit and feasibility. A structured shortlisting process increases speed, reduces noise, and creates a strong foundation for consistent, objective evaluation.

Why it's Challenging @ Scale

  • The model landscape is vast and dynamic: It’s hard to stay current with newly released models, updates, and benchmarks.
  • Criteria vary by use case: What makes a model “high potential” depends on business priorities, input types, constraints, and success measures.
  • Limited visibility into model internals: Many models, especially commercial ones, provide minimal transparency into how they work.
  • Stakeholders may have competing priorities: Different teams may value accuracy, cost, latency, or flexibility differently.
  • Shortlists can become political: Without a clear process, model selection can be influenced more by opinions than by evidence.

Complexity

Medium to High: Maturing this capability requires a clear framework for evaluation readiness, repeatable comparison methods, stakeholder alignment, and the ability to maintain a living list of qualified candidates.

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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 Evaluating and Selecting the Best Model(s) for Your GenAI Solution workshop (2 hrs.) to understand foundational key concepts and explore applied best practices
  • Outlining the Model Evaluation Lifecycle
  • Understanding Model Types and Capabilities
  • Aligning Evaluation to Solution Objectives
  • Comparing Commercial vs. Open Source Options
  • Establishing a Reusable Evaluation Framework
  • 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
  • List Available Models from Trusted Sources: Identify a broad list of open-source and commercial models suitable for enterprise use.
  • Draft an Initial Scoring Matrix: Rate models using simple dimensions such as availability, reputation, documentation, or known risks.
  • Shortlist Top 3-5 Candidates: Select a handful of models to test against one representative task or dataset.
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:
  • Defining Your Model Objectives & Requirements
  • Model Evaluation Data Assessment and Prep
  • Selecting In-Scope Models
  • LLM Evaluation
  • 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 whether shortlisting practices led to meaningful evaluation outcomes in early tests.
  • Define in-scope Processes and Guardrails: Establish clear inclusion criteria and decision checkpoints for model selection.
  • Close any Data or Measurement Gaps: Ensure all models on the shortlist can be evaluated using existing datasets and metrics.
  • 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: Outline when and how new LLMs will be reviewed, tested, and approved.
  • Build Awareness and Finalize Enablers: Provide templates, tools, and communications that support shortlisting efforts across teams.
  • Operationalize Your Comms Plan: Share who is responsible for maintaining and updating the shortlist and how others can contribute.
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 Shortlisting Criteria: Define standard attributes such as licensing, latency, accuracy, hosting options, and support.
  • Create a Model Scoring Framework: Use weighted categories to prioritize models based on your organization’s unique needs.
  • Track Evaluation Readiness for Each Model: Monitor which candidates are ready for immediate testing versus future review.
  • Accelerate Your Adoption: Intensify efforts to embed GenAI across your organization by expanding use cases, increasing user engagement, and removing adoption barriers
  • Enable Teams to Self-Shortlist Models: Provide access to scoring tools, model cards, and approval checklists.
  • Standardize Shortlist Updates: Establish a cadence and ownership model for refreshing the list.
  • Log Outcomes from Prior Selections: Record which models were selected and how they performed post-evaluation.
  • Celebrate Your Wins: Publicly acknowledge team accomplishments to build and sustain adoption momentum
  • Highlight High-Impact Shortlisting Decisions: Showcase where a shortlist led to fast, successful model selection.
  • Share Selection Insights Across Teams: Provide visibility into why certain models were prioritized or removed.
  • Recognize Contributors to the Process: Acknowledge those who built or maintained model scoring and documentation.
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 Shortlisting Tools in Evaluation Platforms: Allow teams to filter and compare model options as part of their daily workflows.
  • Unify Shortlists Across Functions: Ensure that engineering, legal, and business teams are all working from the same approved model pool.
  • Automate Shortlist Alerts: Notify users when new models become eligible or existing ones are deprecated.
  • Leverage Automation: Using GenAI-powered tools and workflows to streamline repetitive tasks, enhance operational efficiency, and reduce manual effort
  • Auto-Populate Model Scorecards: Pull in metadata such as training data source, release date, and pricing to assist comparisons.
  • Use LLMs to Draft Evaluation Summaries: Generate snapshot assessments of model pros, cons, and ideal use cases.
  • Auto-Flag Models for Review: Set triggers based on performance changes, licensing updates, or user feedback.
  • Evolve & Further Accelerate: Continuously refine GenAI strategies based on insights and outcomes, while expanding into more complex or high-impact use cases
  • Refine Scoring Criteria Based on Learnings: Update your framework to reflect what actually drove success across evaluations.
  • Expand Shortlisting to Specialized Models: Include domain-specific or multilingual models as adoption expands.
  • Benchmark Shortlisting Effectiveness: Track whether the shortlist consistently yields high-performing, low-risk candidates.

Key "Watchouts"

As you take action you’ll want to avoid:

  • Overloading the shortlist: Too many models reduce focus and increase evaluation time.
  • Making the process too informal: Without structure, shortlisting can be biased or inconsistent across teams.
  • Neglecting evolving requirements: A model that was a good fit last quarter may no longer meet current needs.
  • Ignoring integration or legal constraints: Some models may be appealing on paper but unsuitable for enterprise use.
  • Failing to revisit poor performers: Past evaluations may not reflect current performance after model updates.

Targeted Benefits

While Shortlisting High-Potential LLMs for Evaluation can be challenging, its benefits are clear and compelling, including:

  • Faster time-to-evaluation: Clear candidate lists reduce ramp-up time and simplify planning.
  • Better model fit: Structured filtering ensures models align with the task, context, and business need.
  • Improved consistency and transparency: Teams use the same criteria and process to select candidates.
  • Lower resource waste: Focused shortlists prevent effort being spent on models unlikely to succeed.
  • Greater agility: A dynamic, living shortlist enables quicker responses to new tools and opportunities.

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Eddie
Accelerated Innovation

Hi, I'm Eddie 👋

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