Inside AskData: How We Slashed Token Consumption by Over 90%

What this post added

This post details the technical evolution of the AskData AI data agent, focusing on the challenges of knowledge representation and retrieval for bridging business vocabulary with structured data. It describes the transition from a V0 approach using off-the-shelf LLMs to a V1 system built on Pinecone Assistant and multiple Pinecone vector indexes. The core technical contribution is the migration to Pinecone Nexus, which provides a unified curation pipeline and adaptive knowledge representation, significantly reducing token consumption and improving query efficiency. The post highlights the development of an eval set based on production traces to guide the Nexus build loop and the technical details of the migration process, including the definition of context payloads for the agent.