Why Prompt Engineering is the New Programming (and how to get started) скачать в хорошем качестве

Why Prompt Engineering is the New Programming (and how to get started)

What is prompt engineering? Is it just about writing better questions? Absolutely not. For technical professionals looking to get ahead in the age of AI, prompt engineering is quickly becoming as essential as front-end development. It’s a discipline that is part psychology, part art, and part algorithms. In this deep dive, we're going beyond the basics to show you how to master this critical skill. The core of prompt engineering is about designing and optimizing a system to get the desired output from a generative AI model. As large language models (LLMs) like GPT, Claude, and Gemini become more accessible, the real bottleneck is no longer the model itself, but your ability to effectively communicate with it. Mastering this skill allows you to build sophisticated language pipelines for a variety of use cases, from legal summaries to automating customer service. 📌 What You'll Learn: The True Meaning of Prompt Engineering: We debunk the myth that prompt engineering is just asking better questions. It is about designing a system, not just writing prompts. Technical Components of a Good Prompt: Get a breakdown of key elements like context framing, constraint setting, and format engineering. Core Prompting Frameworks: Learn the concepts behind Zero-shot, Few-shot, and Chain-of-Thought prompting. We'll explain how these frameworks help with reasoning and consistency. Business Impact & Career Advancement: Understand how prompt engineering directly impacts the accuracy, reliability, and cost-effectiveness of AI outputs. We'll explore why this is an in-demand job title. The Probabilistic Nature of LLMs: Grasp why LLMs are not deterministic, and how minor changes in a prompt can lead to wildly different results. 🕒 Timestamps 0:00 - Prompt Engineering is not what you think 0:07 - Prompt engineering as a discipline 0:51 - What is a prompt? 1:08 - Prompt engineering: part psychology, art, and algorithms 1:40 - The bottleneck for LLM use today 2:08 - What the best prompt engineers do 2:36 - The myth of "just asking better questions" 2:55 - Components of a good prompt (Context, Constraints, etc.) 3:32 - The business case for prompt engineering 4:29 - The probabilistic nature of LLMs 5:58 - Prompting frameworks: Zero-shot vs. Few-shot vs. Chain-of-Thought 7:25 - How to handle context from multiple documents 8:22 - RAG (Retrieval Augmented Generation) framework 9:33 - Business use cases for prompt engineering 9:54 - How to learn prompt engineering 10:22 - The future of the prompt engineering role 👩‍🏫 About the Presenter: Dr. Sindhu Ghanta is Head of Machine Learning at AIClub, with deep industry experience and academic research in AI. Her style? No jargon. Just clear, useful explanations that help you learn fast. 🔗 Google Scholar: https://scholar.google.com/citations?... 🔗 Dive Deeper: Want to cut through the clutter and learn AI using a custom path that is designed specifically for you, keeping your current level in mind? Book a free consultation with AIClub Pro, and we’ll help you create that path and achieve your AI goals. 🔗 Learn More & Subscribe: Subscribe to @Schovia for weekly AI tutorials, simplified tech, and the latest trends. 🔗 Explore More at Schovia: https://schovia.com/ 🔔 Don’t forget to like, comment, and subscribe for more in-depth AI breakdowns every week!