Blockify claims 40x corpus reduction and 2.3x relevance gain over naive RAG. Open-source on GitHub, but lacks benchmark details. Blockify, a new RAG method from developer akshay_pachaar, reduces corpus size by 40x. It also cuts tokens per query by 3x and improves vector search relevance by 2.3x over naive RAG. Key facts Blockify reduces RAG corpus size by 40x. Tokens per query drop by 3x. Vector search relevance improves 2.3x. Open-source implementation on GitHub. Specific benchmark details not disclosed. Blockify, a retrieval-augmented generation technique shared by developer akshay_pachaar, claims dramatic efficiency gains over standard chunk-based RAG. The approach reduces the corpus size by 40x, meaning far fewer documents need to be indexed and searched. Tokens per query drop by 3x, lowering both latency and cost for downstream LLM calls.…