Stop Tuning Your Models and Fix Your Data
InfoWorld, Thursday, September 17th, 2026
Dropping an LLM into a chaotic warehouse produces a confident idiot; model gains were marginal next to data-foundation work.
Building a conversational data agent at Runpod to let teams query infrastructure metrics from Slack, the author found the decisive variable was not the model but the architecture underneath it.
Improving the model produced small incremental gains, while improving the data foundation fundamentally changed the quality and usefulness of responses.
An agent cannot fix bad data, missing joins or undocumented columns; dropped into a web of isolated silos and ambiguous schemas, it simply delivers wrong answers faster and more confidently to everyone. The recommended focus is three pillars of the data foundation: security, quality and observability.