Creating a context-sensitive AI assistant: Lessons from building a RAG application

Vectorize aims to simplify the creation of retrieval-augmented generation (RAG) pipelines for AI applications by integrating an AI assistant directly into its user interface, minimizing the need for users to leave the interface to consult separate documentation. The assistant utilizes a RAG pipeline that transforms unstructured content from various sources, including documentation and user interactions on platforms like Discord and Intercom, into embedding vectors stored in a vector database. By integrating context-sensitive query rewriting and reranking models, the system improves the relevance of retrieved information, ensuring that responses generated by a large language model (LLM) are accurate and contextually appropriate. The interface encourages user interaction by seeding questions based on the user's context, and it employs prompting techniques to prevent the LLM from hallucinating answers. Through ongoing monitoring and feedback collection, Vectorize continually refines the AI assistant to enhance user support while keeping its vector indexes updated in real-time.