Retrieval performance is shaped by chunking, embeddings, indexing, and ranking tradeoffs that are deeply interconnected. Small tuning decisions can dramatically affect relevance, latency, and cost—often in non-obvious ways.
To win, your GenAI solutions must retrieve the right context efficiently while balancing precision, recall, and operational constraints.
When retrieval optimization is piecemeal, GenAI systems struggle to deliver consistent results:
- Context quality: Poor chunking or embedding strategies surface noisy or incomplete context.
- Performance bottlenecks: Retrieval latency and indexing overhead grow as data volumes scale.
- Tradeoff blind spots: Precision and recall are tuned independently, leading to brittle or misleading results.
These issues degrade answer quality, slow response times, and increase infrastructure costs.
In this hands-on workshop, your team systematically tunes and validates retrieval pipelines using structured analysis and targeted experiments.
- Tune document chunking and embedding strategies to improve contextual relevance.
- Filter noisy content while boosting high-value contextual signals.
- Reduce retrieval latency through vector optimization techniques.
- Manage storage and indexing overhead without sacrificing retrieval quality.
- Balance precision and recall to support real-world GenAI use cases.
- Tuning Retrieval Chunking and Embedding Strategy
- Filtering Noise and Boosting Contextual Value
- Reducing Latency Through Vector Optimization
- Managing Storage and Indexing Overhead
- Balancing Precision and Recall in Retrieval
- Improve the quality of retrieved context feeding GenAI responses.
- Tune embeddings and chunking strategies for better downstream performance.
- Reduce retrieval latency while controlling infrastructure costs.
- Make informed precision-versus-recall tradeoffs based on use case needs.
- Establish a repeatable approach for ongoing retrieval optimization.
Who Should Attend:
Solution Essentials
Facilitated workshop (in-person or virtual)
4 hours
Intermediate
Vector databases, retrieval pipelines, embedding models, and guided optimization exercises