This paper from PayPal and NVIDIA quietly kills the myth that agentic AI needs giant models to work. PayPal just published a research paper showing that their biggest performance win did not come from a better prompt, a bigger model, or a clever orchestration trick. It came from
@godofprompt
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AI Goal Tracking Systems Guide
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Ultimate Guide to AI Goal Tracking Systems Transforms how I achieve goals and boost productivity • Automate tasks • Gain personalized insights • Manage goals effectively Click below to read more: https://
godofprompt.ai/blog/ultimate-
guide-to-ai-goal-tracking-systems
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Prompt engineering is about psychology, not precision
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I used to think prompt engineering was about precision. It's actually about psychology. You're not instructing a computer. You're activating patterns in a language model trained on billions of human decisions, consequences, and stakes. Treat it like that.
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Framing consequences for high-stakes LLM responses
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Think about it: When you write an email to a friend → casual
When you write an email that could get you fired → every word matters LLMs learned from both types of text. By framing consequences, you're telling the model: "use the high-stakes mode." -
Why LLMs Respond to Stakes in Text
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You would ask me…Why does this work bro? LLMs are trained on human text and human text is full of stakes. When you add consequences, you're not just giving instructions. You're activating the model's training on how humans think and write when something actually matters.
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Using Gemini Deep Research for Automated Customer Support Analysis
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Gemini Pro tip: you can use deep research feature to analyze all your emails in Gmail I just used it to analyze all of my customer support interactions from thousands of emails and give me a detailed report of pain points, feature requests, and why people churn
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How to master Perplexity for efficient research
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I should honestly charge $99 for this. But I’m giving away my Perplexity Mastery Guide for free. Inside: → How to replace hours of manual research
→ How to get cited, accurate answers
→ How pros actually use Perplexity If you do research for a living, this is gold. -
Using LLMs for Personalized Spaced Revision Plans
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7/ Lock in long-term memory
Most learning evaporates in days. Prompt:
“Create a spaced revision plan for this topic over the next 14 days with mini-tests and summaries.” This is how knowledge sticks. -
Using LLMs for Realistic Exam and Task Simulation
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6/ Simulate real exams or real work
Theory without pressure is useless. Prompt:
“Create a realistic exam or real-world task that tests whether I actually understand this topic. Grade my response harshly.” Painful. Effective. -
Using AI prompts to identify personal learning gaps
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5/ Identify blind spots automatically
Humans don’t know what they don’t know. Prompt:
“Based on my answers so far, identify my weak areas and redesign the next 3 lessons to fix them.” This is what no course can do.