Course Overview
This course provides an in-depth exploration of ChatGPT prompt engineering, a pivotal skill for leveraging the full potential of advanced natural language processing models. Participants will gain a foundational understanding of ChatGPT and its underlying GPT architecture, including the principles of machine learning, transfer learning, and fine-tuning that enable these models to generate human-like text. Through a series of interactive lectures and hands-on exercises, learners will master the art of crafting effective prompts that guide AI responses in desired directions.
The course delves into various techniques such as role-based, chain of thought, zero-shot, and fewshot prompting—equipping students with strategies to handle different types of AI interactions. Advanced sessions will cover systematic prompt engineering tactics, multi-step reasoning, ambiguity handling, bias mitigation, meta prompting, and recursive & iterative prompting. Ethical considerations and responsible AI deployment ensure high-quality output while mitigating biases.
Each module is designed to build upon the previous one, ensuring a comprehensive understanding of how to communicate effectively with AI systems and utilize their capabilities for practical applications. By the end of this course, participants will be adept at creating prompts that can significantly enhance the performance and reliability of AI-driven solutions.
Course Objective
After attending this training program, participants should be able to:
- Understand the basic principles of natural language processing and GPT architecture.
- Learn to craft effective prompts considering context length and response control techniques.
- Apply role-based, chain-of-thought, zero-shot, and few-shot prompting strategies for diverse task handling.
- Develop advanced prompt engineering techniques using explicit instructions and code.
- Practice crafting prompts through interactive exercises.
- Identify and apply strategies to mitigate bias in AI-generated content.
- Discuss ethical considerations and best practices for responsible AI communication.
- Master multi-step reasoning by structuring complex tasks into manageable sub-prompts.
- Learn techniques for handling ambiguity through iterative questioning.
- Use iterative and recursive meta prompting to improve AI response accuracy.
Target Audience
Students, Entry Level, Engineers, Mid Level Managers, Senior Managers
Course Duration
This course runs for two (2) days.
Methodology
Case Studies, Labs
Course Outline
- Overview of ChatGPT and its applications
- Importance of prompt engineering in AI interaction
- Goals and objectives of the course
- Current challenges and opportunities in prompt engineering
- Introduction to key terminology and concepts
- Basic principles of natural language processing
- Introduction to GPT architecture and transformer models
- Token-based processing and attention mechanisms
- Transfer learning and fine-tuning processes
- Understanding model limitations and capabilities
- Crafting effective prompts – core principles
- Context and context length considerations
- Techniques for controlling AI response quality
- Temperature and token limits optimization
- Balancing specificity and creativity in prompts is ChatGPT?
- Prompting ChatGPT
- ChatGPT capabilities and limitations
- Practice writing clear and effective prompts
- Experiment with different prompt structures
- Analyze prompt effectiveness and AI responses
- Receive instructor feedback on prompt design
- Introduction to role-based prompting concept
- Designing effective persona frameworks
- Strategies for implementing role-based prompts
- Case studies of successful role prompting
- Common pitfalls and how to avoid them
- Understanding chain of thought methodology
- Designing prompts that elicit step-by-step reasoning
- Implementation strategies for complex problem-solving
- Applications in educational and analytical contexts
- Measuring effectiveness of chain of thought prompts
- Understanding zero-shot prompting techniques
- Creating effective prompts without examples
- Few-shot learning methodology and implementation
- Selecting optimal examples for few-shot prompting
- Comparative analysis of zero-shot vs. few-shot approaches
- Design role-based prompts for specific use cases
- Create chain of thought prompts for complex problems
- Compare zero-shot and few-shot approaches
- Group discussion and feedback session
- Industry-specific prompt engineering case studies
- Collaborative problem-solving with prompt engineering
- Prompt template creation for common scenarios
- Documenting and sharing effective prompts
- Performance evaluation frameworks
- Breaking complex tasks into logical sub-prompts
- Designing sequential reasoning frameworks
- Managing context across multiple steps
- Error handling in multi-step processes
- Performance optimization techniques
- Identifying ambiguity in prompts and responses
- Techniques for refining vague prompts
- Implementing iterative questioning strategies
- Clarification frameworks and methodologies
- Balancing specificity and flexibility
- Understanding AI bias sources and manifestations
- Detecting biased outputs in AI responses
- Strategies for avoiding stereotypes in persona-based generation
- Techniques for neutralizing political and social biases
- Implementing systematic bias detection and correction
- Generate potentially biased responses to prompts
- Analyze and identify bias patterns
- Rewrite prompts for neutrality and inclusivity
- Group review and discussion of bias mitigation techniques
- Understanding the concept of meta prompting
- Techniques for instructing ChatGPT to generate its own prompts
- Prompt optimization through meta instructions
- Self-improving prompt sequences
- Applications and limitations of meta prompting
- Principles of recursive prompt design
- Teaching ChatGPT to review its own outputs
- Implementation of self-correction mechanisms
- Iterative refinement techniques
- Performance measurement in recursive systems
- Design meta prompts for specific applications
- Create recursive review-and-improve sequences
- Test iterative improvement workflows
- Evaluate performance gains from recursive approaches
- Systematic prompt engineering methodologies
- Using explicit instructions and prompt programming
- Advanced context manipulation techniques
- Managing hallucinations and factual accuracy
- Domain-specific optimization strategies
- Ethical frameworks for AI interaction design
- Privacy considerations in prompt engineering
- Responsible AI deployment practices
- Mitigating manipulation and social engineering risks
- Future directions in responsible prompt engineering
- Design a complete prompt engineering solution for a complex problem
- Implement multiple techniques covered in the course
- Present and justify design decisions
- Peer review and instructor feedback
Itinerary
Time | DAY 1: Description |
08:30 AM – 09:00 AM | Participants attendance |
09:00 AM – 10:30 AM | Module 1: Introduction Module 2: Understanding ChatGPT and GPT Architecture |
10:30 AM – 10:45 AM | Morning Tea Break |
10:45 PM – 12:30 PM | Module 3: Principles of Prompt Engineering Module 4: Hands-on Exercise – Basic Prompt Crafting |
12:30 PM – 01:30 PM | Lunch Break |
01:30 PM – 03:30 PM | Module 5: Role-Based Prompting Module 6: Chain of Thought Prompting |
03:30 PM – 03:45 PM | Afternoon Tea Break |
03:45 PM – 05:00 PM | Module 7: Zero-Shot and Few-Shot Learning Module 8: Hands-on Exercise – Role-Based and Chain of Thought Implementation Module 9: Practical Applications Workshop |
05:00 PM | End of Day 1 |
Time | DAY 2: Description |
08:30 AM – 09:00 AM | Participants attendance |
09:00 AM – 10:30 AM | Module 10: Multi-Step Reasoning Module 11: Hands-on Exercise – Multi-Step Reasoning Module 12: Ambiguity Handling |
10:30 AM – 10:45 AM | Morning Tea Break |
10:45 PM – 12:30 PM | Module 13: Bias Mitigation Module 14: Hands-on Exercise: Bias Detection and Mitigation |
12:30 PM – 01:30 PM | Lunch Break |
01:30 PM – 03:30 PM | Module 15: Meta Prompting Module 16: Recursive and Iterative Prompting Module 17: Hands-on Exercise: Meta and Recursive Prompting |
03:30 PM – 03:45 PM | Afternoon Tea Break |
03:45 PM – 05:00 PM | Module 18: Advanced Prompt Engineering Techniques Module 19: Ethical Considerations and Responsible AI Module 20: Final Hands-on Exercise: Comprehensive Prompt Design Course Recap and Q&A |
05:00 PM | End of Day 2 |
