AI still has a hallucination problem: How MongoDB aims to solve it with advanced rerankers and embedding models

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More To get the best possible result from an AI query, organizations need the best possible data. The answer that many organizations have had to overcome that challenge is retrieval-augmented generation (RAG). With RAG, results are grounded in data from a database. As it turns out, though, not all RAG is the same, and actually optimizing a database for the best possible results can be challenging. Database vendor MongoDB is no stranger to the world of AI or RAG. The company’s namesake database is already being used for RAG, and MongoDB has also launched AI applications development initiatives. While the company and its users — such a medical giant Novo Nordisk — have had success with gen AI, there is still more to be done. In particular, hallucination and accuracy continues to be an issue holding some organizations back from getting gen AI into production. To th ... Read full article.