Post
1284
Building a Ranking System to Enhance Prompt Results: The New PageRank for RAG/LLM
Read full article at https://mltblog.com/4gT62y9
In this document, you will learn how to build a system that decides, among dozens of candidate paragraphs selected from the corpus to answer a prompt, which ones to show in the results, and in what order. The goal is to maximize relevancy while not overwhelming the user with a long, cluttered answer. Think of it as the new PageRank for RAG/LLM, although the algorithm is radically different, and much simpler. The approach is generic and works for all RAG/LLM systems whether based on neural networks or not. It is implemented in xLLM.
The article includes Python code (with links to GitHub) and case study featuring the anonymized augmented corpus of a fortune 100 company, as well as future LLM developments (auto-indexing and LLM for glossary generation).
Read full article at https://mltblog.com/4gT62y9
In this document, you will learn how to build a system that decides, among dozens of candidate paragraphs selected from the corpus to answer a prompt, which ones to show in the results, and in what order. The goal is to maximize relevancy while not overwhelming the user with a long, cluttered answer. Think of it as the new PageRank for RAG/LLM, although the algorithm is radically different, and much simpler. The approach is generic and works for all RAG/LLM systems whether based on neural networks or not. It is implemented in xLLM.
The article includes Python code (with links to GitHub) and case study featuring the anonymized augmented corpus of a fortune 100 company, as well as future LLM developments (auto-indexing and LLM for glossary generation).