In the subsequent chapters, we further analyze three key
parts of RAG in detail. Chapter 4 introduces the retriever of
RAG, how to process corpora to obtain better semantic repre-
sentations, how to mitigate the semantic gap between Query
and documents, and how to adjust the retriever to fit the gen-
erator. Chapter 5 explains how the generator obtains better
generation results by post-processing retrieved documents,
avoiding the ”Lost in the middle” issue, as well as methods to
adjust the generator to fit the retriever. Subsequently, in Chap-
ter 6, we review the current retrieval enhancement methods
from the aspects of the retrieval stage, retrieval data sources,
and retrieval process.
Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey