Predictable Dialogs
Predictable Dialogs
5 months ago
capterra.co.uk
When building applications with generative AI, model behavior is less predictable than traditional software. That’s why automated testing makes a big difference in saving you development time and cost. We teamed with CircleCI to bring you a new course: Automated Testing for LLMOps. In this course, you’ll learn to build a continuous integration workflow to evaluate your LLM applications at every iteration, to catch and fix bugs early, and ship faster and more efficiently. Instructed by Rob Zuber,
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When building applications with generative AI, model behavior is less predictable than traditional software. That’s why automated testing makes a big difference in saving you development time and cost. We teamed with CircleCI to bring you a new course: Automated Testing for LLMOps. In this course, you’ll learn to build a continuous integration workflow to evaluate your LLM applications at every iteration, to catch and fix bugs early, and ship faster and more efficiently. Instructed by Rob Zuber,
694 viewsJan 24, 2024
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After Blowing Trillions on Software, Why Don't IT Pros Learn?
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ieee.org
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Probably the best take I have heard on why there is so much fear around software exposure in private equity and credit because of AIFrom David Sacks"Historically we only had two good exits for software businesses. One was IPO. The other was M&A. Then these big private equity shops came along and gave us a third potential exit. You would sell to them and then they would raise the capital based on one third equity and two third debt. So it was debt financed buyouts. It is something that has been a
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Boring software doesn’t trend, but it wins. It doesn’t break every update. It doesn’t chase shiny features. It just works. Every day. Predictable. Maintainable. Secure. That’s why banks, governments, and businesses rely on it. Flashy software gets attention. Boring software gets users. Reliability beats excitement, every time. That’s why boring software wins.
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Why Tech Professionals Fail When Transitioning Into AI. Many software engineers struggle when moving into AI because AI systems are probabilistic, not deterministic. Traditional software gives predictable outputs. AI models can shift, drift, and fail silently in production. In this video, I explain the biggest AI career transition mistakes: treating AI like just another framework, prioritizing flashy demos over proper evaluation, and confusing prompting with real AI engineering. If you want to b
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