Prompt Engineering
This course is for developers who have completed the LLM Basic training and want to learn the systematic method of Prompt design and optimization. From basic concepts to advanced patterns, from manual design to automated tools, this course helps students master the complete skill system of Prompt Engineering and improve the effectiveness and reliability of LLM applications.
Day 1: Prompt Basics and Engineering Methods
From Trial and Error to Systematic Design
Study Content +
1.1 Prompt Basic Concepts
What is a Prompt: definition, principle and key mechanism
Common types and basic structure of Prompts (Instruction, Input, Output, Constraints)
1.2 Introduction to Prompt Engineering
Why Prompt Engineering is needed: from trial and error to systematic design
The core goals and value of Prompt Engineering
Overview of application scenarios: product design, data processing, dialogue systems, code generation, etc.
How to systematically design and optimize Prompts
1.3 Construction Framework for Efficient Prompts
Analysis of common elements: Persona, Task, Context, Format
Case study: how to build a clear, efficient and robust Prompt from scratch
1.4 Tool Drills and Practical Operations
Use ChatGPT to experience the process of writing and tuning Prompts
Analysis of typical failure cases and optimization suggestions
Learning Objectives +
- โ In-depth understanding of the working principle and basic structure of Prompts
- โ Master the systematic design method of Prompt Engineering
- โ Learn to use the construction framework to design efficient Prompts
- โ Accumulate experience in Prompt optimization through practical operations
Day 2: Advanced Models and Engineering Practices
Advanced Techniques and Automated Optimization
Study Content +
2.1 Prompt Patterns and Advanced Techniques
Analysis of advanced prompt techniques (refer to Google Prompt Engineering Whitepaper): Chain-of-Thought, Few-shot / Zero-shot Prompting, ReAct (Reasoning + Action), Tree of Thought, etc.
Selection and design strategies of Prompt patterns in different scenarios
Deconstruction of typical cases and hands-on practice
2.2 Engineering Management Methods for Prompts
Version control, parameter tuning and reuse strategies for Prompts
Prompt management methods for multi-model adaptation
Introduction to engineering management tools: such as the usage and advantages of LangSmith
2.3 Practice of Automated Optimization Tools: DSPy, etc.
What is DSPy: Prompt orchestration and learning optimization framework
The role and boundary of automated optimization tools
Practical exercise: use DSPy to build and optimize the Prompt process
2.4 Comprehensive Practical Exercises
Group design of Prompt systems in business scenarios
Comprehensive application: framework + model + tools
Group results presentation and comments
2.5 Course Summary and Q&A
Review of core knowledge and competency map
Frequently asked questions and practical suggestions
Advanced learning paths and material recommendations
Learning Objectives +
- โ Master a variety of advanced Prompt modes and techniques
- โ Learn engineering management and version control methods for Prompts
- โ Understand and apply automated optimization tools to improve efficiency
- โ Integrate learned knowledge through comprehensive practical exercises
- โ Establish a complete Prompt Engineering skill system