Are your embeddings tuned for your specific GenAI use cases—or just using defaults?
As GenAI systems mature, embedding quality becomes a primary performance lever, requiring deliberate trade-offs across model choice, chunking strategy, cost, and accuracy.
To win, your GenAI solutions need an embedding approach that is empirically tuned for performance, cost, and fidelity.
The Challenge
Teams relying on generic embedding setups often face:
- One-size-fits-all embeddings: Models and settings are chosen without validating fit to real use cases.
- Suboptimal granularity: Chunk sizes and context windows dilute meaning or increase cost without benefit.
- Unmeasured quality trade-offs: Teams lack objective ways to compare accuracy, speed, and cost impacts.
Poorly tuned embeddings will cap GenAI performance and drive unnecessary cost at scale.
Our Solution
In this hands-on workshop, your team evaluates, tunes, and iterates embedding strategies to maximize GenAI performance for targeted use cases.
- Compare embedding models by use case requirements.
- Select optimal chunk granularity and context windows.
- Test embedding quality using semantic evaluation tasks.
- Evaluate trade-offs between cost, speed, and fidelity.
- Iteratively improve embedding pipelines based on results.
Area of Focus
- Comparing Embedding Models by Use Case
- Selecting Optimal Granularity and Context Windows
- Testing Embedding Quality with Semantic Tasks
- Evaluating Trade-offs: Cost, Speed, and Fidelity
- Iterating Embedding Pipelines for Performance
Participants Will
- Select embedding models aligned to specific GenAI use cases.
- Tune chunking and context strategies for measurable gains.
- Apply semantic tests to evaluate embedding quality.
- Make informed cost and performance trade-offs.
- Establish a repeatable process for embedding optimization.
Who Should Attend:
Solution Essentials
Format
Virtual or in-person
Duration
4 hours
Skill Level
Advanced teams optimizing production GenAI systems
Tools
Multiple embedding models, evaluation datasets, and performance benchmarking frameworks