Sunila Gollapudi

Evaluate generative and analytical models to build Knowledge Graphs and facilitate these augmented, domain-centric Knowledge Graphs to power highly performing RAGs Sunila Gollapudi · Follow Published in Towards Data Science · 17 min read · 9 hours ago — Retrieval-augmented generation (RAG) systems are an advanced combination of generative AI and retrieval-based technologies. The purpose of RAGs is to improve the quality of generated text by incorporating…

Sunila Gollapudi · Follow Published in Towards Data Science · 15 min read · 14 hours ago — In my previous articles, I wrote about using Knowledge Graphs in conjunction with RAGs and how Graph techniques can be used for Adaptive tokenization to build more context-aware LLMs. In this article, I am excited to present my experiments combining Text Embeddings and Knowledge (Graph) Embeddings and observations on RAG performance. I will start by explaining the