Prompt engineering refers to the practice of designing and structuring inputs to large language models so that they produce accurate, useful, and reliable outputs. As language models have grown more capable, the way prompts are written has become an important skill, blending aspects of linguistics, logic, and problem formulation. Over time, several distinct types of prompt engineering have emerged, each suited to different tasks and levels of model guidance. The following discussion presents the main types of prompt engineering in an essay-style narrative, with an example woven into each explanation.
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Case Study – Use of Agentic AI in Search Engine Marketing
Breaking News! Google Specialist releases guide to Agentic AI systems
Antonio Gulli’s comprehensive book covers 21 design patterns for building autonomous AI agents, featuring practical frameworks and technical implementation guides for developers. Antonio Gulli, a Google distinguished engineer, has published a comprehensive 400-page technical guide to building autonomous AI systems, offering detailed blueprints for creating sophisticated artificial intelligence agents. Antonio Gulli, Senior Director and Distinguished Engineer in Google’s CTO Office, announced Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems with a scheduled release date of December 3, 2025. The publication addresses a critical gap in AI development methodology. According to Gulli, building effective agentic systems requires more than just a powerful language model—it demands structured architectural blueprints. “It’s about moving from raw capability to robust, real-world applications,” Gulli stated in the book’s introduction. The guide presents 21 distinct agentic patterns that serve as fundamental building blocks for autonomous AI systems. These patterns range from foundational concepts such as Prompt Chaining and Tool Use to advanced implementations including Multi-Agent Collaboration and Self-Correction frameworks. Each pattern represents a reusable solution to common challenges encountered when building intelligent, goal-oriented systems
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Demystifying Artificial Intelligence using Set Theory
The Evolution of Artificial Intelligence: From Logic to Learning
Artificial Intelligence (AI) has evolved from a theoretical curiosity into a transformative force reshaping industries, societies, and human potential. Its journey spans decades of innovation, marked by paradigm shifts—from symbolic reasoning to statistical learning, and now to generative intelligence. Understanding this evolution reveals not just technological progress, but a deeper narrative about how machines have come to mimic, augment, and even challenge human cognition.
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