Скачать или смотреть Why Your LLMs aka Gen AI is Confident but Wrong - How LLMs Actually Work

Here's why your AI sounds so confident even when it's completely wrong - and understanding this changes everything about how you use it.

🔥 The Problem: Your AI gives confident-sounding responses that can be completely incorrect because LLMs aren't reasoning systems - they're prediction engines. Most people don't understand this fundamental difference.

💡 The Solution: Understand the mechanics, manage the psychology, and focus on context over clever prompting. After my AI confidently suggested a terrible YouTube title that tanked my video, I rebuilt my entire approach.

What I Share:
• Why LLMs sound confident even when wrong (and the psychology that tricks us)
• The car analogy that explains our 0-70mph AI adoption speed
• How 93% accuracy comes from context, not model selection
• Real examples of AI failures and the human-in-the-loop fixes that work

📺 Chapters:
00:00 The Car Flag Walker Analogy
02:00 Next Token Prediction Explained
05:00 Visual Demo - How LLMs Actually Work
07:00 Why LLMs Excel at Code vs Content
09:00 The Confidence Problem
11:00 Psychology - Authority & Automation Bias
13:00 My YouTube Title Disaster
15:00 Context Over Model Selection
18:00 Practical Tips You Can Use Now
23:00 Common Misconceptions to Stop

KEY INSIGHT: It's not about perfect prompting - it's about understanding that you're working with an advanced autocomplete system, not artificial intelligence. Once you grasp this, you can design workflows that leverage its strengths while avoiding its psychological traps.

🎯 The Result: Better AI outcomes through understanding mechanics, not chasing the latest model or prompt technique. This is human-in-the-loop AI, keeping humans in charge while leveraging AI as a powerful tool.

Want more honest takes on AI productivity? This is part of my Human in the Loop experiment, where I test what actually works vs the Silicon Valley hype.

🔗 Links:
Visual LLM Demo: https://poloclub.github.io/transforme...
Fast Show Sketch:    • Fast show - Aren't holidays brilliant?  
Artificial Intel Bias: https://www.nature.com/articles/s4159...
Cognitive Offloading: https://www.psypost.org/catastrophic-...
Insufficient Context: https://research.google/blog/deeper-i...

HITL AI Series: Real experiments in AI productivity, data-driven insights, and what works when you put humans back in the loop instead of abdicating decision-making to algorithms.

Subscribe for more: Evidence-based AI productivity, honest breakdowns of what works vs hype, and practical workflows you can implement immediately.

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#HumanInTheLoop #AIProductivity #LLMExplained #AIReality #NextTokenPrediction #AIWorkflow #ContentCreation #DataDriven