Anthropic just reduced the error rate of RAGs by 67% using a ridiculously simple method. They add important context to small text chunks before storing them, which improves accuracy later. Instead of just saying “the company grew by 3%,” it includes details like which company and when,... See more

Sounds fancy. Why do we care? GAR involves taking the source documents and having an LLM enrich them, prior to indexing. For example, the LLM might... * Generate titles for documents that are missing them * Standardize author names/formats* Extract dates, URLs, citations and other elements that might be valuable to search as separate fields* Create... See more