How to put generative AI directly into a dbt pipeline with plain SQL
Google Cloud on wiring BigQuery's AI SQL functions into dbt pipelines, from sentiment scoring to LLM-generated product briefings.
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Why most of your dbt models aren't in a hurry, and what that's worth in Snowflake credits
Most models in a dbt DAG aren't on the critical path. Espresso AI and Authentic Insurance on turning that slack into real savings.
How Ecolab used an AI migration agent to shrink 84,000 lines of legacy SQL by 6x
Ecolab and Datafold used an AI migration agent to rebuild a decade of tangled Snowflake logic as clean, documented dbt models.
How Checkr cut an hours-long metrics investigation to minutes by engineering AI context on dbt and Omni
Checkr gave its AI agent a structured dbt context layer, cutting a support-metric investigation from hours to minutes.
Why a semantic layer is the difference between a 96% and an 80% accurate AI analyst
Why conversational analytics stalls and the data fundamentals that make it reliable.
Why dbt Labs thinks AI agents will finally make the semantic layer stick
Why AI agents can make semantic layers stick, if teams can solve the trust problem.
What Okta learned governing context for AI agents at scale
Okta rebuilt how it governs context for AI agents, using dbt as the single source of truth.
dbt v2 lands: faster parsing, one shared adapter layer, and a docs experience that skips the giant manifest.json
dbt v2 bundles adapters, speeds up parsing, and replaces manifest.json with smaller, queryable files for people and AI.
Showing our work: a controlled experiment in context engineering
We ran one AI skill 80 times against four context sources. Engineered summaries cut cost 48.5% and run time 43.2%.
How a data scientist at Kilo built the business context layer his AI agents were missing
How a data scientist built an AI context layer for organizational knowledge a dbt project alone can't hold.