# agentset-rag-notes - Page: https://shared.btbytes.com/p/agentset-rag-notes - Date: 2025-10-20 Notes on [RAG](https://shared.btbytes.com/p/rag) from the creator of [GitHub - agentset-ai/agentset](https://github.com/agentset-ai/agentset) --  open-source platform to build, evaluate, and ship production-ready RAG and agentic applications. It provides end-to-end tooling: ingestion, vector indexing, evaluation/benchmarks, chat playground, hosting, and a clean API with first-class developer experience. ## What made the difference - **Query Generation** -- review the thread and generate a number of semantic + keyword queries. We processed all of those queries in parallel, and passed them to a reranker. This made us cover a larger surface area and not be dependent on a computed score for hybrid search. - **Reranking** -- "the highest value 5 lines of code you'll add. The chunk ranking shifted _a lot_. More than you'd expect. Reranking can many times make up for a bad setup if you pass in enough chunks." - **Chunking Strategy** -- * this takes a lot of effort, you'll probably be spending most of your time on it.* - **Metadata to LLM** -- Injecting metadata (title, author, etc.) improves context and answers by a lot. - **Query routing**: many users asked questions that can't be answered by RAG (e.g. summarize the article, who wrote this). We created a small router that detects these questions and answers them using an API call + LLM instead of the full-blown RAG set-ups. ## Stack - turbopuffer for [vector-databases](https://shared.btbytes.com/p/vector-databases) - UnstructuredIO for chunking - Zerank by [ZeroEntropy - Smarter, Faster Models for Search](https://www.zeroentropy.dev) for Reranking - GPT-4.1