課程資料
課程簡介
Role Prompting, Few-Shot Prompting, Hallucination, Iterative Prompting, Structure, Summarization, Inference, Expanding
Welcome to the Captivating World of LLM Prompt Engineering!
This course empowers you to unlock the true potential of Large Language Models (LLMs), regardless of your experience level. Whether you're a seasoned professional or a curious beginner, this comprehensive program equips you with the skills to become a master of LLM prompt engineering.
Master the Art of Crafting Powerful Prompts:
Diverse Task Applications: Craft effective prompts tailored to various tasks, including generating informative summaries, creating captivating stories, or even translating languages, all through the power of well-designed prompts.
Advanced Techniques Exploration: Move beyond the basics and delve into advanced concepts like iterative prompting, where you refine your prompt based on the LLM's initial output. Additionally, explore few-shot learning, allowing you to achieve impressive results even with limited data.
Core LLM Concepts Demystified: Gain a solid understanding of fundamental LLM properties like statelessness and quantization. Explore how these properties impact prompt design and LLM behavior. Learn to identify and mitigate potential hallucinations in LLM outputs.
Unleash LLM Capabilities Through Hands-on Learning:
Code Walkthroughs Deepen Understanding: Go beyond theory with interactive code walkthroughs using Lamma 2 as a platform. Actively explore code examples to gain practical experience in setting up, configuring LLMs, working with advanced models (e.g., quantized models), and leveraging specialized notebooks like AWQ for optimized workflows.
Real-World Applications Solidify Skills: This course emphasizes the practical application of LLM prompt engineering. Learn how to tailor prompts to solve specific real-world problems, ensuring accurate and creative AI outputs. Translate your newfound knowledge into tangible results.
Structured Learning Journey for Success:
Clear and Concise Explanations: Simplify complex topics with bite-sized lessons and clear explanations.
Interactive Learning Approach: Utilize a variety of learning methods, including interactive code walkthroughs, to reinforce understanding and foster your development as an LLM prompt engineering expert.
Progressive Curriculum Design: Build your expertise step-by-step, starting with the fundamentals of LLMs and prompt engineering and progressing to advanced techniques.
Embrace the Future of AI Interaction:
By mastering LLM prompt engineering, you'll be at the forefront of the human-AI interaction revolution. This course equips you with the skills and knowledge to confidently navigate this exciting field and unlock the true power of LLMs. Let's embark on this journey together and explore the boundless possibilities of AI!
課程章節
- 7 個章節
- 25 堂課
- 第 1 章 Introduction
- 第 2 章 Set up and Prompt template
- 第 3 章 LLM Properties
- 第 4 章 Prompt Engineering Basic Guidelines
- 第 5 章 Better Prompting Techniques
- 第 6 章 LLM using Langchain - Ollama & RAG
- 第 7 章 Image Prompt Engineering
課程內容
- Artificial Intelligence, Deep Learning, Data Science, Generative AI, LLM, Large Language Model, Prompt Engineering, llama, GPTQ, GGUF, AWQ, zero shot Learning, One shot Learning, Guidelines, Inference, Expanding, Summarizing, Prompt creation
此課程所涵蓋的技能
評價
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AAyush Srivastava
Crisp
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PPoojasri K
I've noticed that the explanation often repeats the word "basically" and uses it continuously, which can be distracting. Additionally, the section on LLM configuration parameters seems incomplete. The definitions of the parameters are either missing or unclear, even though there are typically seven or eight key configuration parameters. Because of this, the information is difficult to understand. I suggest providing clear, accurate definitions for each LLM configuration parameter to improve clarity and usefulness.
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MMarius Ionescu
The course begins with a promising structure, but it quickly becomes apparent that the organization lacks consistency and clarity. While some sections are overly detailed on minor points, more critical aspects are unfortunately glossed over, which affects the overall coherence and depth of the content. Implementation-wise: - Audio quality varies noticeably between chapters, with the microphone occasionally producing subpar results. - The English used throughout is serviceable but could benefit from refinement for better clarity and engagement. - The delivery lacks energy and conviction, giving the impression that the presenter is not fully invested in the material. - At times, especially during longer segments, the visuals are sparse and the narration feels monotonous, which may reduce viewer engagement. Overall, the course resembles a loosely curated set of LLM-related videos one might find on YouTube. It serves as a reminder that subject matter expertise alone does not make for effective teaching—presentation, structure, and enthusiasm are equally important.
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GGaurav Kumar
Good