A Deep Dive into Advanced RAG Re-Ranking Methods
Advanced re-ranking introduces powerful models and pipelines, but small design and tuning choices can dramatically affect latency, relevance, and scalability. This workshop focuses on advanced methods that push beyond basic re-ranking while remaining production-aware.
To win, your GenAI solutions must apply advanced re-ranking models that are tuned to domain relevance and deployed through efficient index-rank pipelines.
When teams adopt advanced re-ranking without rigor, predictable issues emerge:
- Model selection uncertainty: Teams struggle to choose between transformer-based, pairwise, and listwisererankers without clear evaluation criteria.
• Underperforming cross-encoders: Powerful models are deployed without proper optimization, limiting relevance gains.
• Production inefficiency: Advanced re-ranking pipelines introduce latency and cost that are not aligned with real-world constraints.
These challenges result in expensive pipelines that fail to deliver proportional improvements in retrieval quality.
In this hands-on workshop, your team explores, tunes, and operationalizes advanced RAG re-ranking methods through applied exercises.
- Explore transformer-basedrerankersand where they outperform simpler approaches.
• Apply pairwise and listwise ranking models to improve ordering of retrieved passages.
• Enhance cross-encoder performance through targeted optimization techniques.
• Tune re-ranking models using domain-specific relevance data.
• Design and deploy efficient index-rank pipelines suitable for production environments.
Exploring Transformer-Based Rerankers
Using Pairwise and Listwise Ranking Models
Enhancing Cross-Encoder Performance
Tuning on Domain-Specific Relevance Data
Deploying Efficient Index-Rank Pipelines
• Evaluate advanced reranking models using clear relevance criteria.
• Apply pairwise and listwise approaches to improve passage ordering.
• Optimize cross-encoder models for stronger relevance signals.
• Tune rerankers on domain-specific relevance datasets.
• Design index-rank pipelines that balance quality, latency, and cost.
Who Should Attend:
Solution Essentials
Virtual or in-person
4 hours
Advanced; experience with RAG pipelines and re-ranking required
Transformer rerankers, cross-encoders, ranking models, and evaluation environments