Most AI failures are data engineering failures
Bad chunking strategy, a stale vector index, garbage source data, no evals, a shared billing pool nobody owns. None of these are model problems. They are pipeline problems, and data engineers already know how to fight pipeline problems.
That is what this site covers, organized into three pillars: research translated into production defaults, failure modes from the 2 AM pager, and verdicts on the tools touching your data.
How to read this site
- One claim per section. Sections open with the claim, not a topic label.
- Big numbers, linked sources. Stats render large and every one links to its source.
- Every failure ships with its fix. Stated plainly, no consulting-speak.
- Sources at the bottom of every post. A claim without a source does not ship.
Where the short version lives
The Instagram account is the distribution channel: carousels and reels, a few times a week. This site is the long-form home. Every post here started as an account post and got the full breakdown it deserved.
Follow @dataeng.ai on Instagram
Faceless on purpose
No author headshots, no personal brand. Faceless on purpose: the work is the voice, and the evidence carries it. Check the sources. Run the numbers yourself.