Transforming Banking with Advanced AI: GenAI, RAG, and Multi-Agent Systems by Kevin Suys

RAG (Retrieval Augmented Generation) helps pre-select relevant documents before sending to LLMs, improving accuracy and reducing hallucinations

Data quality and chunking strategies are crucial for effective RAG implementation - including naive chunking (word/sentence/paragraph) and more advanced contextual approaches

Multilingual embeddings allow for language-agnostic vectorization, enabling responses in different languages while maintaining knowledge base in one language

Input and output guardrails help validate queries and responses, preventing harmful content and ensuring high-quality, relevant answers

Hybrid retrieval approaches combining traditional keyword search with vector search can improve result relevance

Re-ranking retrieved documents helps improve diversity and reduces redundant or too-similar results

Multi-agent systems with specialized agents (coding, business knowledge, etc.) can handle complex tasks more effectively than single LLM approaches

Regular evaluation of RAG systems should measure precision, recall, answer relevancy and faithfulness to source material

Token optimization and context window management are important for cost and performance efficiency

Modular architecture allows switching between different models (open/closed source) as technology evolves rapidly