Accelerated Innovation

Prepping Your Evaluation Data

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Prepping Your Evaluation Data

Description

This capability focuses on cleaning, structuring, and transforming data to ensure it is ready for use in Large Language Model (LLM) evaluation. It includes steps like formatting inputs, removing noise, and aligning dataset structure to match evaluation goals and technical constraints.

Why it's Important

Even high-quality data can fail to support meaningful evaluation if it is not properly prepared. Inconsistent formatting, missing labels, or misaligned data structures can distort results and introduce unnecessary risk. Preparing evaluation data ensures that LLM tests are efficient, reproducible, and accurately reflect real-world conditions. It also creates a foundation for automation, governance, and scaling across additional teams and use cases.

Why it's Challenging @ Scale

  • Data formats vary widely across sources: Teams often need to reconcile inconsistencies in file types, schemas, or annotation formats.
  • Lack of standardized preprocessing steps: Without clear guidance, teams duplicate effort or overlook essential transformations.
  • Manual cleanup does not scale: Cleaning and structuring data often requires repetitive, time-intensive work that slows progress.
  • Missing or noisy data reduces accuracy: Incomplete records, typos, or mislabeled fields can compromise evaluation outcomes.
  • Tooling limitations: Many enterprises lack integrated platforms to support streamlined and repeatable data preparation.

Complexity

High: Maturing this capability requires establishing reusable workflows, embedding quality control checkpoints, and ensuring that data preparation meets both technical and business requirements at scale.

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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.

  • 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
  • Standardize Inputs for One Use Case: Clean and format a small dataset to create a reusable input structure.
  • Pilot a Lightweight Preprocessing Script: Automate key cleanup steps such as removing duplicates or correcting formats.
  • Document Manual Steps Taken: Create a checklist or notebook that outlines prep decisions for future reuse.
  • 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 how your data preparation methods support LLM evaluation accuracy and repeatability.
  • Define in-scope Processes and Guardrails: Establish standardized steps for data cleaning, transformation, and documentation.
  • Close any Data or Measurement Gaps: Ensure that your prepared data supports all required metrics and reflects expected structure.
  • 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: Prioritize evaluation areas where data prep standards can be easily applied or reused.
  • Build Awareness and Finalize Enablers: Provide tools, templates, and guidance to help teams prepare data efficiently.
  • Operationalize Your Comms Plan: Share prep lessons learned, success stories, and reusable scripts to promote consistency.
  • Formalize Your Best Practices: Document and standardize what’s working to ensure consistent, scalable success across teams and use cases
  • Create Standard Preprocessing Pipelines: Build reusable scripts and notebooks for common cleaning and structuring tasks.
  • Publish Data Prep Guidelines: Document preferred formats, naming conventions, and transformation requirements.
  • Embed Prep Review in Evaluation Planning: Require teams to validate and approve data readiness before LLM testing begins.
  • Accelerate Your Adoption: Intensify efforts to embed GenAI across your organization by expanding use cases, increasing user engagement, and removing adoption barriers
  • Scale Prep Practices to More Teams: Roll out standardized data prep processes to new GenAI evaluation teams or regions.
  • Offer Training on Tooling and Techniques: Provide workshops or demos on how to use common prep scripts and platforms.
  • Incorporate Prep Metrics into Evaluation Dashboards: Track prep time, errors, or reuse rate as part of broader evaluation metrics.
  • Celebrate Your Wins: Publicly acknowledge team accomplishments to build and sustain adoption momentum
  • Spotlight Time Savings from Automation: Share how standard prep reduced manual work and sped up evaluations.
  • Highlight Well-Prepared Datasets: Showcase data assets that meet prep standards and improved LLM performance.
  • Recognize Prep Champions: Call out individuals or teams that contributed reusable scripts, templates, or documentation.
  • Streamline & Embed: Integrate GenAI into core workflows while eliminating friction points to make usage seamless and routine
  • Embed Prep Tools in Evaluation Platforms: Make cleaning and formatting tools accessible within model testing environments.
  • Require Prep Validation in Registries: Ensure each dataset in use for evaluation is tagged as “prepped” and validated.
  • Unify Prep Templates Across Teams: Standardize data input formats across GenAI use cases to enable cross-team reuse.
  • Leverage Automation: Using GenAI-powered tools and workflows to streamline repetitive tasks, enhance operational efficiency, and reduce manual effort
  • Automate Preprocessing Pipelines: Use AI or workflows to convert raw inputs into standardized, ready-to-evaluate datasets.
  • Auto-Flag Prep Errors and Inconsistencies: Build automated checks that detect missing labels, formatting issues, or structural mismatches.
  • Suggest Prep Enhancements with AI: Recommend improvements to dataset structure or formatting based on model input history.
  • Evolve & Further Accelerate: Continuously refine GenAI strategies based on insights and outcomes, while expanding into more complex or high-impact use cases
  • Update Prep Standards Based on Feedback: Refine prep guidelines based on what drives the most accurate or reliable model results.
  • Expand Prep to Additional Modalities: Apply structured prep approaches to image, audio, or multimodal LLM inputs.
  • Benchmark Prep Readiness Across Teams: Track adoption and effectiveness of prep processes across groups and geographies.

Key "Watchouts"

As you take action you’ll want to avoid:

  • Skipping basic cleanup steps: Neglecting tasks like deduplication or null value handling leads to noisy evaluation results.
  • Reworking prep for every use case: Lack of standard templates or tooling increases inconsistency and effort.
  • Assuming structure equals quality: Well-formatted data may still contain hidden biases, noise, or irrelevance.
  • Underestimating prep time: Teams often fail to budget for the time and tools required to get data evaluation-ready.
  • Leaving prep undocumented: Untracked transformations make evaluation results hard to interpret or replicate.

Targeted Benefits

While Prepping Your Evaluation Data can be challenging, its benefits are clear and compelling, including:

  • Faster time to evaluation: Prepped datasets accelerate testing cycles and shorten model comparison timelines.
  • Higher-quality evaluation results: Clean, structured data increases confidence in model performance outcomes.
  • Greater reuse and scalability: Standard prep pipelines make it easier to scale evaluations across use cases and teams.
  • Improved traceability and governance: Documented prep processes support auditing and compliance.
  • Reduced manual effort: Automation and reuse reduce the burden on teams and free up time for higher-value work.

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Eddie
Accelerated Innovation

Hi, I'm Eddie 👋

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