Prompt Engineering

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. ...