We explore how Retrieval-Augmented Generation (RAG) can transform quantitative research workflows by enabling accurate, context-aware answers sourced from technical documentation.
- How to build RAG pipelines to handle complex, domain-specific queries efficiently
- What are the key technical challenges, like: retrieval strategies, query handling, grounding and answer generation
- How to train and evaluate these systems
- What are the best practices and learnings for designing robust, scalable AI assistants for quant and business decision-making