Prompt Engineering for Everyone is designed to provide you with a comprehensive understanding of how to effectively interact with large language models (LLMs) by crafting well-structured prompts. Through an exploration of key concepts such as transformer architecture, attention mechanism, and prompt modifiers, this course equips learners with hands-on skills for improving LLM outputs across a variety of applications like text summarization, information extraction, and code generation. You will also explore advanced techniques such as chain-of-thought prompting, self-consistency, and contrastive CoT, ensuring you can optimize LLM performance in practical settings. By the end of the course, you’ll be well-prepared to use LLMs in diverse business, creative, and technical contexts.
What you'll learn
- Understand the basics of LLMs and prompt engineering.
- Explore LLM parameters and prompt structure for better results.
- Use prompt modifiers for different formats, tones, and styles.
- Learn zero-shot, one-shot, and few-shot prompting techniques.
- Perform hands-on tasks like summarization, classification, and chatbots.
- Master advanced techniques like Chain-of-Thought and ExpertPrompt.
- Address risks and challenges of LLMs in enterprise environments.
- Practice prompt tuning and manage interpretability risks.
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