@hackproduct9: Your model isn't the hard part. Everything around it is. ⚡ Teams ship the feature, then find out they can't tell why it got worse. 12 LLMOps concepts — as a loop, not a list 👇 🟢 BUILD 1. Datasets — curate, version, hold out. The holdout never leaks. 2. Prompt versions — a prompt is code. If you can't diff it, you can't debug it. 3. Model pinning — pin the version, never "latest." Providers deprecate on their clock. 🔵 PROVE 4. Eval sets — golden cases you must never break. 5. LLM as judge — score at scale, then audit the judge against humans. 6. Regression gate — a drop below threshold blocks the release. No green run, no ship. 🟡 SHIP 7. Canary — 1% of traffic, and you pick the abort metric before you start. 8. Shadow — run the new model silently on real traffic. The answer never reaches a user. 9. Rollback — undo in seconds. It's a button, not a plan. 🟣 OPERATE 10. Tracing — one request ID, end to end. Logs alone won't find the slow hop. 11. Cost tracking — tokens are the invoice. Price per resolved ticket, not per call. 12. Feedback loop — a thumbs-down becomes tomorrow's eval case. 👀 The one nobody says: 12 feeds back into 1. Every LLMOps diagram draws a pipeline with an arrow that stops. It doesn't stop. The thumbs-down you got this morning should end up in the eval set that blocks next week's deploy. Feedback that dies in a dashboard isn't LLMOps. It's logging. The rule: your eval suite owns the deploy button. Most teams find out the expensive way. The provider silently updates the model, quality drops 8%, and nobody notices for three weeks because there was no golden set to fail. 📸 Screenshot the grid. Save it before your next AI architecture review. Follow @hackproduct — we turn scary AI-engineering concepts into things you can actually ship. ⚡ . . #LLMOps #AIengineering #machinelearning #MLops #LLM

HackProduct

Open In TikTok:

Region: US

Sunday 09 August 2026 18:07:14 GMT