Beyond Vectors: Evolving GenAI through Transformative Tools and Methods - Alison Cossette

Beyond basic vector databases, graph-based RAG systems enable deeper context understanding and relationship tracking between documents and responses

Key elements for high-quality RAG applications include proper data chunking, embeddings generation, document similarity analysis, and conversation tracking

Use community detection and centrality algorithms to identify clusters of similar documents and important content nodes in your dataset

Monitor conversation patterns and response times to optimize retrieval performance and identify gaps in knowledge coverage

Graph databases allow tracking relationships between entities (companies, people, documents) while preserving context that vector databases alone cannot capture

Log and analyze user interactions to understand common questions, response consistency, and conversation flows

Examine document clusters and similarity scores to identify redundant content and opportunities for pre-generated responses

Validate dataset quality through basic statistics (word length, chunk size) and visual analysis of document relationships

Consider both tightly clustered and widely distributed document patterns when optimizing retrieval, depending on use case

Leverage data science techniques like KNN similarity and community detection to improve RAG system performance over time