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What are the 3 Types of Prompt Engineering? 🚀

What are the 3 Types of Prompt Engineering? 🚀 Prompt engineering is a crucial aspect of natural language processing (NLP) and artificial intelligence (AI) that often goes unnoticed by the end-users. Yet, it plays a pivotal role in determining the accuracy and effectiveness of AI models in various applications such as chatbots, language translation, content generation, and more. In this article, we will delve into the fascinating world of prompt engineering, exploring the three primary types and their real-world significance.  1. Explicit Prompts🤖 Explicit prompts are perhaps the most straightforward and commonly used type of prompt in NLP. These prompts explicitly instruct the AI model to perform a specific task or generate content with a defined format. They leave little room for ambiguity, making them ideal for scenarios where precision is paramount.  Real-world Application: Text Summarization ✍️ In text summarization, explicit prompts play a crucial role in extracting the most sal

How AI Prompt Engineers Empower Machines to Think and Respond

How AI Prompt Engineers Empower Machines to Think and Respond 🤖 AI technologies have advanced rapidly! Machines can now play chess ⚔, drive cars🌇, respond helpfully🤓, analyze images📸 and more. However, most don’t understand how modern AI works behind the scenes. This blog will explore the crucial role of AI prompt engineering in empowering machines to think and respond like humans. At their core, advanced AI systems are not general intelligent beings like humans🚶. They have been programmed by engineers to perform specific tasks instead of possessing a unified sense of self or consciousness👥. So how exactly do AI engineers design prompts and contexts to empower machines to act intelligently within limited domains? Large Language Models and Pre-Training The most capable AI assistants💬, chatbots💭 and natural language processing systems are based on large language models📚. Models like GPT-3, BERT, T5 contain hundreds of billions of parameters allowing them to learn from enormous t