AI Prompt Engineering Course - Australia

Introduction to Prompt Engineering

1. What prompt engineering is
2. Understanding the purpose of prompts
3. Role of prompts in AI systems
4. Prompting Large Language Models
5. Prompting AI chatbots
6. Real-world applications of prompt engineering

Understanding Context, Tokens, and Model Limitations

1. Understanding context in AI interactions
2. What tokens are
3. How token limits affect prompts and responses
4. Context limitations
5. Understanding model constraints
6. Designing prompts around model limitations

Understanding AI Models and Their Behavior

1. Overview of popular AI models
2. GPT
3. BERT
4. T5
5. Differences between AI model types
6. How model behavior influences prompting strategies

How AI Models Process Prompts

1. Understanding tokenization
2. Introduction to attention mechanisms
3. How prompts are interpreted
4. Relationship between prompt structure and generated responses
5. Why similar prompts can produce different outputs

Common Prompt Engineering Pitfalls

1. Ambiguous instructions
2. Missing context
3. Overly broad prompts
4. Conflicting instructions
5. Poorly defined output requirements
6. Recognizing ineffective prompts
7. Improving weak prompts

Zero-Shot Prompting

1. Understanding zero-shot prompting
2. Giving instructions without examples
3. Selecting tasks suitable for zero-shot prompts
4. Improving zero-shot responses
5. Practical zero-shot applications

One-Shot Prompting

1. Understanding one-shot prompting
2. Providing a single example
3. Using examples to guide format and behavior
4. Improving consistency with one-shot prompts

Few-Shot Prompting

1. Understanding few-shot prompting
2. Providing multiple examples
3. Teaching patterns through examples
4. Improving output consistency
5. Designing effective few-shot examples

Chain-of-Thought Prompting

1. Introduction to Chain-of-Thought prompting
2. Breaking complex tasks into steps
3. Supporting structured reasoning
4. Designing prompts for multi-stage problems
5. Using reasoning-oriented prompts appropriately

Role-Based Prompting

1. Assigning roles to AI models
2. Defining expertise and perspective
3. Adding task-specific context
4. Using role prompts for professional scenarios
5. Combining roles with instructions and constraints

Iterative Prompt Refinement

1. Treating AI outputs as drafts
2. Refining prompts through multiple iterations
3. Asking AI to improve previous responses
4. Adding missing context
5. Adjusting tone and detail
6. Testing multiple prompt versions

Using Constraints and Guidelines

1. Defining output length
2. Specifying tone
3. Setting format requirements
4. Providing rules and restrictions
5. Defining target audiences
6. Using examples and reference structures
7. Improving consistency through constraints

Multi-Step Prompting

1. Breaking complex objectives into smaller tasks
2. Structuring sequential prompts
3. Managing dependencies between steps
4. Improving complex AI workflows
5. Building more reliable multi-stage outputs

Prompt Chaining

1. Understanding prompt chaining
2. Connecting multiple prompts together
3. Passing outputs between stages
4. Creating repeatable AI workflows
5. Managing complex tasks using prompt sequences

Modular Prompt Design

1. Building reusable prompt components
2. Separating context, instructions, examples, and output requirements
3. Creating standardized prompt structures
4. Reusing prompt modules across workflows
5. Improving maintainability and consistency

Fine-Tuning vs. Prompt Engineering

1. Understanding prompt engineering
2. Introduction to fine-tuning
3. Differences between prompting and model customization
4. When prompting is sufficient
5. When model customization may be required
6. Selecting the appropriate approach for a use case

Using External Tools with Prompts

1. Connecting prompts with external tools
2. Working with APIs
3. Using databases with AI workflows
4. Understanding tool-assisted AI systems
5. Combining model responses with external information

Debugging Prompts

1. Identifying why prompts fail
2. Diagnosing unclear instructions
3. Testing prompt variations
4. Reviewing inconsistent outputs
5. Correcting structural issues
6. Improving reliability

Optimizing Prompt Performance

1. Improving clarity
2. Reducing unnecessary instructions
3. Increasing output relevance
4. Testing multiple prompt structures
5. Improving consistency
6. Developing reusable high-performing prompts

Prompt Engineering for Content Creation

1. Blog content generation
2. Copywriting
3. Idea generation
4. Content restructuring
5. Tone adaptation
6. Improving AI-generated content through prompting

Prompt Engineering for Code Generation

1. AI-assisted programming
2. Generating code from instructions
3. Explaining code
4. Debugging assistance
5. Technical documentation
6. Creating clearer prompts for development tasks

Prompt Engineering for Business

1. Customer support applications
2. Marketing applications
3. Business communication
4. Creating role-specific prompts
5. Developing reusable business prompt templates
6. Adapting prompts to organizational requirements

Prompt Engineering in Specialized Industries

1. Healthcare applications
2. Legal applications
3. Education applications
4. Understanding domain-specific requirements
5. Adapting prompts to specialized contexts
6. Importance of human review in sensitive domains

Bias and Responsible Prompt Design

1. Understanding bias in AI responses
2. Identifying biased outputs
3. Designing prompts more responsibly
4. Ethical considerations in prompt creation
5. Reducing unintended or harmful outputs

Avoiding Harmful AI Outputs

1. Toxicity risks
2. Misinformation
3. Misleading outputs
4. Reviewing AI-generated information
5. Using human oversight
6. Designing prompts to improve output safety

Privacy in Prompt Engineering

1. Understanding privacy risks
2. Avoiding confidential information in prompts
3. Protecting personal and business data
4. Considering data sensitivity when using AI tools
5. Responsible handling of prompt inputs

Transparency and Explainability

1. Understanding transparency in AI interactions
2. Communicating appropriate AI usage
3. Designing clearer AI interactions
4. Understanding limitations in explainability
5. Building user trust

Legal Considerations of AI-Generated Content

1. Legal implications of generated content
2. Copyright considerations
3. Ownership considerations
4. Responsible publishing
5. Reviewing generated content before business use

Building Trustworthy AI Systems

1. Responsible prompt engineering practices
2. Combining prompting with human review
3. Improving reliability
4. Reducing risk
5. Building trust through responsible AI interactions

Future Trends in Prompt Engineering

1. Evolution of prompt engineering
2. Emerging prompting techniques
3. Changes in AI-model capabilities
4. Increasing use of tool-connected AI
5. Role of prompt engineering in future AI systems