Pre-Processing & Enriching Your Data - Metadata Enrichment
Without meaningful metadata, even well-prepared data lacks the context GenAI systems need to reason accurately, retrieve relevant information, and produce reliable outputs.
To win, your GenAI solutions need data that is intentionally enriched with trustworthy, well-managed metadata.
When metadata is missing or poorly managed, teams struggle with:
- Shallow context: Data lacks the descriptive signals GenAI needs to interpret meaning and relevance.
- Disconnected sources: Internal and external data cannot be reliably linked or reasoned over together.
- Eroding trust: Inconsistent or stale metadata undermines confidence in downstream AI outputs.
Weak metadata enrichment will reduce GenAI accuracy, limit reuse, and introduce hidden risk.
In this hands-on workshop, your team designs and applies practical approaches to enrich data with meaningful, well-governed metadata for GenAI use.
- Enrich datasets with contextual metadata that improves GenAI understanding.
- Link internal and external data sources through shared metadata.
- Add descriptive and semantic layers to support reasoning and retrieval.
- Improve data usability for downstream GenAI tasks.
- Maintain metadata integrity across data lifecycles.
Enriching Data with Contextual Metadata
Linking External and Internal Data Sources
Adding Descriptive and Semantic Layers
Improving Data Usability for Downstream Tasks
Maintaining Metadata Integrity
- Identify metadata that materially improves GenAI performance.
- Design enrichment strategies aligned to real GenAI tasks.
- Connect related data across systems using shared context.
- Improve retrieval, grounding, and reasoning outcomes.
- Maintain trustworthy metadata as data evolves.
Who Should Attend:
Solution Essentials
Virtual or in-person
4 hours
Intermediate
Metadata models, enrichment workflows, and guided GenAI-ready examples