Prompt Engineering

“I think the next year’s Turing test will truly be the one to watch—the one where we humans, knocked to the canvas, must pull ourselves up… the one where we come back. More human than ever.” — Brian Christian (author of The Most Human Human) The AI Masquerade: Why Personality Now Wins the Game Artificial intelligence is no longer confined to tasks of logic or data wrangling. Today’s advanced language models have crossed a new threshold: the ability to convincingly impersonate humans in conversation. A recent study found GPT-4.5, when given a carefully crafted prompt, was judged more human than actual humans in a Turing test (Jones & Bergen, 2025). This result hinged not simply on technical fluency, but on the generation of believable personality—a voice that shows emotion, adapts to social context, occasionally makes mistakes, and mirrors human conversational rhythms. ...

📺 Watch this first: Jimmy O. Yang on “Guess How Much” “Because the art is in the savings — you never pay full price.” 💬 “Guess How Much?” — A Philosophy for AI Developers In his stand-up comedy, Jimmy O. Yang jokes about how Asian families brag not about how much they spend, but how little: “Guess how much?” “No — it was $200!” It’s not just a punchline. It’s a philosophy. And for developers building LLM-powered applications for small businesses or individual users, it’s the right mindset. ...

Introduction In the recent paper LLM-Gomoku: A Large Language Model-Based System for Strategic Gomoku with Self-Play and Reinforcement Learning, by Hui Wang (Submitted on 27 Mar 2025), the author demonstrates how Large Language Models (LLMs) can learn to play Gomoku through a clever blend of language‐based prompting and reinforcement learning. While at first glance this sounds like yet another AI approach to a classic board game, the innovative aspects of integrating prompts, self‐play, and local move evaluations offer fresh insights into how LLMs might tackle real‐world decision problems—especially where traditional AI often struggles to handle complexities or requires enormous labeled data. ...

“Fine-tuning and prompting don’t just teach—sometimes, they mislead. The key is knowing how much to trust new information.” — Cognaptus Insights 🧠 Introduction: When Models Learn Too Eagerly In the world of Large Language Models (LLMs), LoRA fine-tuning and prompt engineering are popular tools to customize model behavior. They are efficient, modular, and increasingly accessible. However, in many practical scenarios—especially outside elite research labs—there remains a challenge: Enterprise-grade LLM deployments and user-facing fine-tuning workflows often lack structured, scalable mechanisms to handle input quality, model confidence, and uncertainty propagation. ...