MLOps Pipelines: From Experiment to Production Models
KU
Kiril Urbonas
9 months ago • 1 min read•Updated 3 days ago•2 views
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Production RAG Reliability — Making LLM Answers Trustworthy
A demo RAG app is easy; one users trust is not. This is the map for reliable retrieval-augmented generation: grounding, evaluation, retrieval quality, guardrails, and safe rollout.
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Shadow Testing and Canary Releases for LLM Changes
A prompt tweak or model bump can quietly wreck answers everywhere. Ship LLM changes the way you ship risky code: gate, shadow, canary, roll back.
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Debugging RAG Retrieval — Why It Returns Garbage
When RAG answers go sideways, the model usually isn't the problem. Here's the top-to-bottom checklist we run to find where retrieval actually breaks.
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