PineconeChatGPT and alternative generative AI tools often create false information as they are not designed to possess knowledge. In an interview with ZDNet, Edo Liberty, CEO of startup Pinecone, explained this issue and the quest to create authoritative outputs from GenAI. Liberty highlights the need to fetch relevant information from the vector database and combine it with the language model to improve interactions.
The vector database is part of a growing field called “retrieval-augmented generation” (RAG), aiming to supplement the neural network’s capabilities with external input. Liberty, an industry expert in vector databases, recognized the potential of AI language models and developed vector databases to enhance their performance. The vector database represents data using vector embeddings and facilitates similarity searches.
Liberty’s implementation of vector databases within large organizations such as Amazon revealed that it was a complex database system that required a specific architecture and infrastructure. The ability to handle challenges related to storage, scale, and system management is critical for a vector database’s effectiveness in production.
The vector database is complementary to large language models like GPT-4. Combining queries with stored vectors can help reduce the prevalence of false information generated by these models. Additionally, it can support multimodal search capabilities for images, audio, and text, contributing to the reduction of hallucinations.
Efforts to improve large language models involve integrating them with memory-like structures to mimic database functionality; Microsoft and other parties have explored similar strategies. Overall, RAG techniques like vector databases hold promise for refining generative AI outputs and reducing false assertions.
