What’s the first thing you usually change when an AI project works in the example but not in your version?
Summary
A discussion prompt asking AI practitioners about common troubleshooting steps when an AI project fails to replicate an example, focusing on typical issues and time-consuming fixes.
Similar Articles
What’s one thing you wish you had tested before putting an AI agent into production?
A social media post asking practitioners about unexpected challenges and untested issues when deploying AI agents to production, highlighting gaps between development and real-world use.
What is one AI task you stopped automating because it caused more work than it saved?
I think we talk a lot about what AI agents can automate, but not enough about what they should not automate. Sometimes a workflow looks perfect on paper, but once it runs in the real world there are too many exceptions, wrong decisions, or manual fixes. Have you ever automated something and later turned it off because it created more work? What went wrong? I’m more interested in real examples than successful demos.
How are you handling recovery when AI agents fail mid-task in production? and How often this happens for you?
A discussion query asking developers how they handle recovery when AI agents crash mid-task in production, exploring approaches like restarting, persisting state, using checkpoints, or manual inspection.
I keep seeing people give up on AI because it gives them generic junk. 9 times out of 10 it's the prompt. I coach professionals on getting AI actually working for their job, and the same fix solves most of it.
Advice on improving AI outputs by crafting better prompts and building reusable systems, rather than generic requests.
Where AI agents actually break in real workflows (not demos)
A discussion on where AI agents fail in real workflows, highlighting issues with coordination, reliability under messy inputs, and the challenge of reducing human intervention in production.