Explains how rerankers improve search and AI results by reordering retrieved documents for better precision and relevance.
Argues that building a good search engine is more critical for effective RAG than just using a vector database, as poor retrieval misleads AI.
An experiment comparing retrieval performance of chunked vs. non-chunked documents using long-context embedding models like BGE-M3.
Explains how to implement document retrieval with the Azure OpenAI Assistants API using a custom RAG approach, as the retrieval tool is not yet natively supported.
A technical guide exploring the OpenAI Assistants API, covering its core concepts and demonstrating how to create an assistant with the Code Interpreter tool.
A developer shares experiments building LLM-powered tools for research, reflection, and planning, including URL summarizers, SQL agents, and advisory boards.
