An AI eval can report 90% pass rate and be lying to you with no errors thrown. Three silent bugs I hit: grading fixes inflate scores, broken judges get logged as fails, and a judge can hit 90% agreement while ignoring problems it already spotted.
Article below -
Open-weight vs. frontier models isn't the real question in enterprise AI.
The real question is: how do you orchestrate both?
A thread on why "pick a side" thinking is already outdated
Prompt engineering is dead. Loop engineering is taking over.
The industry is shifting from single-prompt chatbots to autonomous, stateful agentic loops.
Here is the 5-stage blueprint of how AI architecture evolved:
Most AI failures aren't model failures. They're evaluation failures.
Benchmarks assume clean prompts. Users don't.
"Update billing" or "Change my card"
Same intent. Production breaks.
Read more:
https://t.co/b2ouBNmLM3
#AI#LLM#RAG#AIEvaluation#MachineLearning
Most AI products do not really fail in demos but after they go live. In POCs, answers seem right, and it feels ready to ship. But real usage is different. That’s where things start to change.
Article below - https://t.co/1go84yDQMq
#GenerativeAI#AIEngineering#AIProducts
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