AI Prompt Engineering - corso completo

Unlock the Power of Large Language Models (LLMs) with Advanced Prompt Engineering Techniques

This course is designed for both beginners and those with existing knowledge, offering a deep dive into the most effective prompt engineering techniques to maximize the output from Large Language Models. Learn how to analyze LLM responses and refine your prompts to align with model capabilities and desired outcomes.

We'll cover a critical analysis of language model performance and improvement strategies, including evaluating responses to specific prompts, comparing outputs against reliable references, and understanding inter-annotator agreement. Explore iterative refinement techniques, data augmentation, and active prompting to optimize your LLM performance.

The course utilizes platforms like Hugging Face and OpenAssistant for hands-on online experience with LLMs.

Key topics include:

• N-shot Prompting: Empower LLMs to learn from minimal examples, significantly boosting their ability to generalize and produce relevant responses. Discover how to craft effective prompts with just a few examples.
• Chain of Thought (CoT): Enable LLMs to mimic human-like reasoning by guiding them through a series of self-consistent steps, resulting in more detailed and insightful answers. Learn to implement CoT for improved reasoning.
• Generated Knowledge Prompting: Leverage the pre-existing knowledge within LLMs to obtain in-depth and accurate responses, ideal for information retrieval and complex queries. Master techniques for extracting knowledge from LLMs.
• Directed Stimulus Prompting: Guide LLMs to generate highly customized and targeted responses by providing specific instructions within your prompts. Explore how to control and direct LLM output.

The course culminates in a practical, hands-on module focused on applying the techniques learned. Gain practical experience in crafting high-performing prompts for real-world applications of LLMs.