Even strong models underperform when trained or grounded on poorly profiled, inconsistent, or biased data. Without disciplined data optimization, teams struggle to understand which data changes actually improve results.
To win, your GenAI solutions must be powered by data that is relevant, well-annotated, unbiased, and measurably tied to output quality.
When data optimization is informal or incomplete, GenAI quality improvements stall:
- Understanding data fitness: Rely on assumptions instead of profiling data for relevance, coverage, and gaps.
- Maintaining data quality: Work with inconsistent annotations, weak metadata, or hidden bias across sources.
- Measuring data impact: Make data changes without clear benchmarks linking them to output quality.
These issues lead to unpredictable performance, slow iteration cycles, and wasted data investment.
In this hands-on workshop, your team applies structured techniques to evaluate, refine, and benchmark GenAI data assets.
- Profile data sources to assess relevance, coverage, and alignment with target use cases.
- Evaluate and improve annotation quality and consistency across datasets.
- Enrich metadata and domain labels to improve retrieval, grounding, and filtering.
- Identify and eliminate redundancy and sources of bias in training or reference data.
- Benchmark the impact of data changes on GenAI output quality using controlled comparisons.
- Profiling Data Sources for Relevance and Coverage
- Improving Annotation Quality and Consistency
- Enriching Metadata and Domain Labels
- Eliminating Redundancy and Bias
- Benchmarking Data Impact on Output Quality
- Assess whether existing data sources are fit for their intended GenAI use cases.
- Improve annotation practices to increase consistency and signal quality.
- Apply richer metadata and labeling to strengthen downstream GenAI behavior.
- Reduce redundancy and bias that degrade model and system performance.
- Quantify how data changes affect output quality and decision confidence.
Who Should Attend:
Solution Essentials
Facilitated workshop (in-person or virtual)
4 hours
Intermediate
Shared collaboration space (virtual whiteboard or equivalent) and shared notes