LLM EaaS Data Prep Best Practices
Model selection slows down when evaluation artifacts aren’t structured for reuse. This workshop defines the criteria, metadata, and catalog backbone needed to make evaluation outputs searchable, comparable, and decision-ready across teams.
Leave with a practical approach to prepare, organize, and operationalize evaluation data—so teams can choose models faster and with confidence.
Many organizations evaluate models, but can’t scale model decision-making because the data and artifacts aren’t structured for reuse and discovery.
- Model evaluation outputs aren’t reusable: Results and insights live in scattered documents and dashboards, making it hard to compare models across use cases or time.
- There’s no consistent model “catalog language”: Without a shared metadata schema, teams can’t quickly find models, interpret fit, or understand constraints.
- Recommendations are informal and don’t improve: Selection relies on opinions and one-off experience rather than structured criteria and feedback-driven learning.
When evaluation data isn’t prepared and cataloged, Model EaaS becomes slow—and model decisions remain inconsistent.
We help teams build the data prep and cataloging approach that turns evaluation into an enterprise service—not a recurring project.
- Define criteria for evaluating and cataloging LLMs: Establish a consistent set of criteria that supports model comparison and decision-making across use cases.
- Design a metadata schema to support catalog functions: Create a practical schema that enables search, filtering, and interpretation—so teams can quickly assess fit.
- Build recommendation approaches for use-case fit: Define how criteria and metadata translate into model recommendations that are explainable and repeatable.
- Integrate catalogs with internal evaluation tools: Connect catalog information to the tools teams already use so evaluation insights are accessible at decision time.
- Improve recommendations through feedback loops: Establish a mechanism to learn from outcomes and refine recommendations as usage expands.
- Defining criteria for evaluating and cataloging LLMs
- Designing a metadata schema to support catalog functions
- Building recommendation algorithms for use-case fit
- Integrating catalogs with internal evaluation tools
- Enhancing recommendations through feedback loops
- Define the criteria and metadata needed to evaluate and catalog models consistently
- Identify the biggest gaps preventing reuse and comparison of evaluation outputs today
- Draft a practical metadata schema that supports search, filtering, and decision-making
- Outline an approach to generate repeatable recommendations for use-case fit
- Leave with a plan to integrate catalogs with evaluation tools and improve via feedback loops
- Define the criteria and metadata needed to evaluate and catalog models consistently
- Identify the biggest gaps preventing reuse and comparison of evaluation outputs today
- Draft a practical metadata schema that supports search, filtering, and decision-making
- Outline an approach to generate repeatable recommendations for use-case fit
- Leave with a plan to integrate catalogs with evaluation tools and improve via feedback loops
Who Should Attend:
Solution Essentials
Facilitated workshop (in-person or virtual)
4 hours
Advanced
Virtual whiteboard and shared document workspace