2 posts tagged with "llm"

An AI agent is only as reliable as the data it reasons from. Feed it nulls, duplicates, or stale data and it will produce confident, coherent, and wrong answers — often without any obvious signal that something is off. The LLM doesn't know what it doesn't know.

dbt's testing framework is the right place to enforce data quality before data reaches your agents. This post covers a layered testing strategy that catches the most common failure modes before they become AI failures.

The most common failure mode in enterprise AI projects is asking an LLM questions about your business data and getting confidently wrong answers. The model doesn't know your revenue figures, your customer data, or your internal processes — it only knows what it was trained on.

Retrieval-Augmented Generation (RAG) fixes this by giving the model the relevant context it needs at query time, retrieved from your actual data. The surprising part: you probably don't need a new data infrastructure to do it. Your existing warehouse already has the data — you just need the retrieval layer on top.