When preparing emails and documents for embedding into a vector database as part of a Retrieval-Augmented Generation (RAG) pipeline, it is crucial to follow best practices to enhance retrieval quality and minimize errors. This involves cleaning the data to reduce vector noise and prevent hallucinations, which are false or misleading information generated by AI models. Effective strategies include removing irrelevant content such as signatures, disclaimers, and repetitive headers in emails, as well as standardizing formats and ensuring consistent data structures. These practices are particularly important when handling diverse document types like newsletters, system notifications, and mixed-format files, as they help maintain the integrity and accuracy of the information being processed. This matters because clean and well-structured data ensures more reliable and accurate AI model outputs.
