AI+ Prompt Engineer Level 1

Description

AI+ Prompt Engineer Level 1 (1 Day)

Program Detailed Curriculum

Executive Summary
The AI+ Prompt Engineer Level 1 Certification Program introduces learners from diverse backgrounds and
levels of expertise to the fundamental principles of artificial intelligence and prompts engineering.
Covering the history, concepts, and applications of AI, machine learning, deep learning, neural networks,
and natural language processing, the program also delves into best practices for designing effective
prompts that harness the capabilities of AI models to their fullest potential. Through a combination of
theoretical instruction and practical exercises, including project-based learning sessions, participants
acquire the skills needed to create and utilize prompts across various domains and objectives.

Course Prerequisites
Understand AI basics and how AI is used – no technical skills required
Willingness to think creatively to generate ideas and use AI tools effectively

Module 1

Foundations of Artificial Intelligence (AI) and Prompt Engineering

1.1 Introduction to Artificial Intelligence
Brief History and Evolution of AI: Explore the origins of AI, from early concepts and theoretical foundations to
modern advancements.
AI’s Impact Across Industries: Overview of how AI is transforming sectors such as healthcare, finance, education,
and entertainment, illustrating its widespread influence.

1.2 History of AI
Key Milestones: Highlight significant milestones in AI development, including the creation of the Turing Test, the
establishment of AI as an academic discipline, and the advent of neural networks.
Evolution of AI Technologies: Discuss the evolution of AI technologies over time, emphasizing the transition from
simple algorithms to complex neural networks and machine learning models.

1.3 Basics of Machine Learning
Introduction to Machine Learning: Define machine learning and explain its critical role as a subset of AI, focusing
on how machines learn from data.
Supervised vs. Unsupervised Learning: Provide an overview of the differences between supervised and
unsupervised learning, including typical applications and examples.

1.4 Deep Learning and Neural Networks
Basics of Deep Learning: Introduce deep learning as an advanced form of machine learning that uses neural
networks to model complex patterns in data.
Neural Networks Explained: Dive into the structure and function of neural networks, detailing how they mimic the
human brain to process information and learn.

1.5 Natural Language Processing (NLP)
Overview of NLP: Outline what NLP is and its significance in enabling machines to understand, interpret, and
generate human language.
Applications of NLP: Briefly explain common applications of NLP in AI, such as speech recognition, text analysis,
and language translation.

1.6 Prompt Engineering Fundamentals
Defining Prompt Engineering: Clarify what prompt engineering is and its importance in the field of AI, especially in
tasks involving natural language processing and generative AI.
Principles and Techniques: Introduce the basic principles and techniques of prompt engineering, including how
to craft effective prompts to guide AI models towards desired outputs.

Module 2

Principles of Effective Prompting

2.1 Introduction to the Principles of Effective Prompting
Overview of Prompting Importance: Discuss the pivotal role of effective prompting in enhancing AI interactions
and performance.
Enhancement through Well-Crafted Prompts: Explain how carefully constructed prompts can significantly
improve the quality of AI-generated responses.

2.2 Giving Direction
Creating Clear, Concise Prompts: Share strategies for formulating prompts that are both clear and concise, ensuring
AI understands the desired direction.
The Role of Specificity: Highlight the importance of specificity in prompts to minimize ambiguity and ensure AI-
generated responses meet expectations.

2.3 Formatting Responses
Specifying AI Response Formats: Techniques for directing AI on the desired format of responses, such as lists,
paragraphs, or tables, to suit different informational needs.
Influence of Formatting on Utility: Discuss how the chosen format can affect the utility and applicability of AI-
generated content in various contexts.

2.4 Providing Examples
Context Setting with Examples: Explore how including examples in prompts can set context and guide the AI
towards the intended style or approach.

Clarifying Tasks and Outcomes: Use examples to make tasks clearer to the AI, ensuring the expected outcomes are
well understood.

2.5 Evaluating Quality
Assessing AI Responses: Outline methods for evaluating the quality of AI responses to ensure they meet the set
criteria and objectives.
Addressing Common Issues: Strategies for identifying and rectifying common issues in AI outputs through iterative
prompt refinement.

2.6 Dividing Labor
Breaking Down Complex Tasks: Techniques for deconstructing complex tasks into smaller, more manageable
prompts to effectively leverage AI capabilities.
Sequential Prompt Structuring: How to structure prompts sequentially to ensure comprehensive coverage of
complex topics or multifaceted tasks.

2.7 Applying The Five Principles
Real-World Application: Engage with practical exercises and case studies demonstrating the application of the five
principles of effective prompting.
Documentation Tools: Introduction to worksheets and one-pagers as tools for participants to practice these
principles and document their insights and progress.

2.8 Fixing Failing Prompts
Identifying Prompt Failures: Methods to diagnose why prompts may fail and strategies for their correction.
Hands-on Activity: A practical session where participants work to revise and improve a set of failing prompts,
applying learned strategies for effective prompting.

Module 3

Introduction to AI Tools and Models

3.1 AI Tools and Models Landscape
Understanding AI Tools and Models: An introduction to the variety of AI tools and models available today in the
business landscape.
Generative AI vs. Traditional Models: Explore the differences between generative AI models like GPT and
traditional AI models, highlighting their unique applications and impacts.

3.2 Deep Dive into ChatGPT
Architecture of ChatGPT: Explore the intricate design and functionality behind ChatGPT, delving into its
architecture, algorithms, and natural language processing capabilities.
Capabilities and Limitations: Explore ChatGPT’s capabilities and limitations in natural language processing,
understanding its strengths and areas where improvement is needed.
Real-world Applications: Explore ChatGPT’s diverse applications in various industries, from customer service to
content creation, uncovering its real-world impact and potential.

3.3 Exploring GPT-4
Advancements in GPT-4: Explore improved context understanding, nuanced response generation, expanded
knowledge base, and enhanced language coherence for diverse applications in GPT 4.
GPT-4 in Practice: Discover how GPT-4 transforms industries with practical demonstrations in business, healthcare,
and various sectors.

3.4 Revolutionizing Art with DALL-E 2
Introduction to DALL-E 2: Discover the transformative impact of DALL-E 2 on visual creativity, exploring AI’s role in
redefining artistic expression and innovation.
Using DALL-E 2 for Creative Projects: Learn to harness the power of DALL-E 2 for creative expression in art and
design with expert techniques and practical tips.

3.5 Introduction to Emerging Tools using GPT
Claude-instant-100k and DALL-E-3: Explore cutting-edge models redefining AI’s creative potential through rapid
generation and imaginative image synthesis.
Comparative Analysis: Contrast Claude-instant-100k
advancements in this module on emerging AI tools.
&
DALL-E-3
with
established
models,
showcasing

3.6 Specialized AI Models
StableDiffusionXL and Llama-2-70b-Groq: Discuss the specialized applications of these models for image
generation and beyond.
Practical Applications: Unlock the power of StableDiffusionXL and Llama-2-70b-Groq for advanced data analysis
and AI-driven decision-making in diverse industries.

3.7 Advanced AI Models
Features of Claude-2-100k, Mistral-Medium, and Gemini-Pro: Detail the capabilities and ideal use cases for each
advanced model.
Model Selection Strategies: deployment efficiency.
Learn to select optimal models tailored to project requirements, enhancing AI

3.8 Google AI Innovations
Google-PaLM Overview: Dive into Google’s Pathways Language Model, analyzing its structure, applications, and
transformative influence on NLP and AI technology.
Integrating Google Models: Dive deep into Google AI’s application across products/services, dissecting its
integration, impact, and future implications for users and technology.

3.9 Comparative Analysis of AI Tools
Strengths and Weaknesses Across Models: Explore AI tools and models critically, discerning strengths and
weaknesses to make informed decisions for practical applications.
Selection Criteria for Projects: Outline how to choose the most appropriate AI tool or model based on specific
project requirements.

3.10 Practical Application Scenarios
Designing Effective Use Cases: Create hypothetical scenarios to demonstrate the application of AI tools in solving
real-world problems.
Tailoring Selection Based on Needs: Discuss the importance of matching AI tools to project specifics for optimal
outcomes.

3.11 Harnessing AI’s Potential
Synthesis of Key Insights: Summarize the module’s core teachings and the strategic application of AI tools and
models.

Looking Forward: Embrace continual learning amid AI evolution: adapt and thrive in dynamic landscapes through
ongoing skill enhancement and technological fluency.

Module 4

Mastering Prompt Engineering Techniques

4.1 Zero-Shot Prompting
Understanding Zero-Shot Learning: Explore zero-shot learning techniques and prompt engineering strategies to
enable models to generalize without task-specific training data.
Designing Zero-Shot Prompts: Master crafting prompts to guide AI without prior examples. Delve into strategies
for precision and effective AI interaction techniques.

4.2 Few-Shot Prompting
Leveraging Few-Shot Learning: Utilizing few-shot learning to enhance AI model performance with minimal
examples.
Crafting Few-Shot Prompts: Techniques for incorporating example inputs into prompts to provide better context
and understanding.

4.3 Chain-of-Thought Prompting
Encouraging “Thinking Aloud” in AI: Highlight the strategies to guide AI through verbalizing its thought process
on complex problems.
Structuring Chain-of-Thought Prompts: Understand how to design prompts that lead AI through a logical, step-
by-step reasoning process.

4.4 Ensuring Self-Consistency in AI Responses
Promoting Internal Consistency: Explore techniques ensuring AI outputs coherence, logic, and consistency: from
error detection to robust validation in complex systems.
Self-Checking Mechanisms: Learn methods to guide AI in evaluating its responses accurately and consistently,
ensuring reliability and quality in decision-making processes.

4.5 Generate Knowledge Prompting
Fostering Creativity in AI: Unlock AI’s potential to innovate by synthesizing existing data, fostering creative
solutions and generating novel ideas. Enhance problem-solving skills.
Innovative Prompt Crafting: Explore techniques for crafting creative prompts to inspire AI innovation through
hands-on exercises and practical applications in this module.

4.6 Prompt Chaining
Sequential Prompting for Complex Tasks: Linking prompts in a sequence to address multifaceted tasks or compile
comprehensive information.
Applications of Prompt Chains: Master prompt chain techniques with diverse examples and case studies,
empowering practical application and skill development in various scenarios.

4.7 Tree of Thoughts: Multiple Solutions Exploration
Multipath Solution Exploration: Dive into AI’s problem-solving versatility with Multipath Solution Exploration,
mastering diverse techniques to tackle complex challenges with innovation
Creativity and Diversity in Responses: Discover cutting-edge approaches to enrich AI solutions, fostering creativity
and adaptability through diverse enhancement strategies and methodologies.

4.8 Retrieval Augmented Generation
Augmenting AI with External Data: Learn to enhance AI capabilities by integrating diverse external data sources,
refining responses, and uncovering deeper insights for optimization.
Improving Output Quality: Explore retrieval augmentation’s impact on response quality through diverse real-world
use cases in this insightful and practical module.

4.9 Graph Prompting and Advanced Data Interpretation
Graphical Data in Prompting: Learn to harness the power of graphs and other complex data structures for
advanced AI applications
Insight Generation from Non-Textual Data: Strategies to enable AI to interpret and derive insights from graphical
or complex data formats.

4.10 Application in Practice: Real-Life Scenarios
Practical Application Exercises: Hands-on tasks designed to apply prompt engineering techniques in real-world
settings.
Cross-Industry Use Cases: Exploration of the application of various prompting techniques across different sectors.

4.11 Practical Exercises
Optional Practical Exercises: A collection of exercises for those seeking additional practice to refine their prompt
engineering skills.
Challenging Projects: Projects and prompts designed to test and enhance learners’ abilities in crafting effective AI
prompts.

Module 5

Mastering Image Model Techniques

5.1 Introduction to Image Models
Overview of Generative Image Models: Introduction to the evolution and impact of generative image models on
visual content creation.
Distinguishing Image Models: Comparative analysis of models like DALL-E, Stable Diffusion, highlighting their
unique use cases and capabilities.

5.2 Understanding Image Generation
Principles of Image Generation: Discover the intricacies of image generation through neural networks, exploring
cutting-edge techniques and their applications in visual synthesis.
Visual Content Creation: Explore how image models interpret text prompts to create visual content, bridging
language and imagery in innovative ways.

5.3 Style Modifiers and Quality Boosters in Image Generation
Enhancing Image Quality: Master techniques to enhance image quality and manipulate artistic styles through
guided prompts for compelling visual storytelling and expression.
Practical Examples: Demonstrating how specific prompt adjustments can modify image attributes like style and
resolution.

5.4 Advanced Prompt Engineering in AI Image Generation
The Essence of Prompt Engineering in Image Generation: Master prompt engineering in AI image generation to
harness textual cues to guide models, shaping output’s relevance, accuracy, and artistry.

Embracing Weighted Terms: Enhance AI prompts for nuanced image creation, allowing control over specific
elements for richer visual content.
Negative Prompts in AI Image Generation: Explore implications and countermeasures for AI-generated images,
addressing biases, harmful content, and fostering responsible AI development
Power of Weighted Terms in AI Image Generation: Explore how weighted terms shape AI image generation,
leveraging their power to enhance visual outputs and improve model performance.

5.5 Prompt Rewriting for AI Image Models
Understanding Prompt Influence: Analyze the impact of prompts on behavior, cognition, and culture, delving into
theories and practical implications for diverse contexts.
Prompt Adjustments in AI Image Generation: Master techniques to refine AI image generation promptly,
optimizing quality and efficiency through strategic adjustments. Enhance creative output effectively.
Case Studies and Practical Applications of Prompt Rewriting in AI Image Generation: Explore AI image
generation through case studies, learning practical applications, and mastering prompt rewriting techniques for
creative outputs.

5.6 Image Modification Techniques: Inpainting and Outpainting
Inpainting and Outpainting: Master the art of digital image manipulation through inpainting and outpainting
techniques, unlocking creative possibilities in visual content enhancement.
Inpainting Techniques: Explore advanced methods to reconstruct missing or damaged parts of images seamlessly
using various inpainting techniques. Practical applications emphasized.
Outpainting with AI Models: Unlock creativity through advanced AI models, mastering outpainting techniques to
expand artistic horizons and create captivating visuals beyond conventional limits.

5.6 Image Modification Techniques: Inpainting and Outpainting
Inpainting and Outpainting: Master the art of digital image manipulation through inpainting and outpainting
techniques, unlocking creative possibilities in visual content enhancement.
Inpainting Techniques: Explore advanced methods to reconstruct missing or damaged parts of images seamlessly
using various inpainting techniques. Practical applications emphasized.
Outpainting with AI Models: Unlock creativity through advanced AI models, mastering outpainting techniques to
expand artistic horizons and create captivating visuals beyond conventional limits.

5.7 Realistic Image Generation
Overview of Realistic Image Creation: Explore principles and techniques behind realistic image creation: lighting,
textures, shaders, rendering engines, and compositing for stunning visual effects.
Techniques for Generating Realistic Characters: Explore character development through psychology, backstory
creation, and dialogue crafting to breathe life into your narratives authentically.
Ensuring Consistency Across Characters: Learn to maintain character consistency across scenes and platforms for
immersive storytelling through traditional methods and AI tools.

5.8 Realistic Models and Consistent Characters
Realistic Image Creation Methods: Learn techniques for creating realistic images, including rendering, lighting,
texturing, and post-processing.
Realistic Models and Consistent Characters: Explore creating believable characters and worlds through realistic
modeling techniques in this course focused on narrative consistency and depth.

5.9 Practical Application of Image Model Techniques
Applying Image Model Techniques in Real-World Scenarios: Learn to deploy image models effectively, solving
real-world problems with practical applications, from classification to object detection.
Implications and Opportunities in Image Model Techniques: Explore diverse image model techniques, uncovering
their implications and seizing opportunities for innovation in various applications.

Module 6

Project-Based Learning Session

6.1 Introduction to Project-Based Learning in AI
Importance of Project-Based Learning in AI: Explore AI through hands-on projects to grasp concepts deeply,
fostering critical thinking and problem-solving skills for real-world applications.
Understanding Project-Based Learning: Explore theory, design, implementation. Enhance teaching skills for
engaging, student-centered learning experiences. Foster critical thinking and collaboration.
Traditional vs. PBL in AI Education: Compare traditional lecture-based AI education with Project-Based Learning
(PBL), focusing on hands-on experience and real-world problem-solving strategies.

6.2 Selecting a Project Theme
Choosing Relevant Themes: Offer guidance on how to select project themes that resonate with participants’
interests and learning goals, ensuring engagement and motivation.
CTheme Examples: Present examples of diverse project themes, including both text-based and image-based AI
applications, to inspire creativity and innovation.

6.3 Project Planning and Design in AI
Project Structuring: Outline how to define clear project goals, choose appropriate AI models, and apply prompt
engineering techniques effectively.
Scope, Timeline, and Resources: Discuss the importance of realistic project scope, timeline planning, and efficient
resource allocation to ensure project feasibility and success.

6.4 AI Implementation and Prompt Engineering
Practical Application: Demonstrate the use of prompt engineering techniques in creating effective AI interactions
tailored to the project’s objectives.
Adaptation and Refinement: Explain how to iteratively refine prompts and AI model choices based on feedback
and project progress to achieve better alignment with goals.

6.5 Integrating Text and Image Models
Combining Models for Comprehensive Projects: Explore strategies for the successful integration of text and image
AI models to enhance project complexity and outcomes.
Integration Case Studies: Share examples of projects that have effectively combined different AI models for
innovative and enriched results.

6.6 Evaluation and Integration in AI Projects
Project Outcome Assessment: Introduce methods for evaluating the success of projects in meeting initial
objectives and the importance of benchmarking results.
Iterative Improvement: Highlight the process of making iterative refinements based on evaluations, focusing on
prompt adjustments and model selection for enhanced project performance.

6.7 Engaging and Effective Project Presentation
Presentation Preparation: Guide participants on how to prepare and present their projects, emphasizing clarity,
coherence, and the ability to convey technical details.
Feedback Session: Facilitate a constructive feedback session, encouraging the sharing of insights, challenges
encountered, and problem-solving strategies.

6.8 Guided Project Example
Walkthrough of a Guided Project: Provide a detailed example of a project from conception to completion,
illustrating the application of learned concepts in a structured manner.
Encouragement for Independent Projects: Motivate participants to undertake their projects, using the guided
example as a blueprint for their initiatives.

Module 7

Ethical Considerations and Future of AI

7.1 Introduction to AI Ethics
Overview of AI Ethics: An introduction to the ethical considerations essential in AI development and deployment,
emphasizing the critical role of ethics in shaping responsible AI technologies.
Ethics in Prompt Engineering: Discuss the significance of incorporating ethical considerations in prompt
engineering and AI model deployment to ensure responsible use and application.

7.2 Bias and Fairness in AI Models
Understanding AI Bias: Explore the sources and impacts of biases inherent in AI models and datasets, highlighting
the importance of recognizing these biases.
Mitigating Bias for Fairness: Present strategies for identifying and mitigating bias in AI-generated content,
ensuring fairness and equity in AI applications.

7.3 Privacy and Data Security
AI and Privacy Concerns: Address privacy issues related to AI development and interaction, including concerns over
data handling and user privacy.
Best Practices for Data Security: Outline best practices for maintaining privacy and ensuring data security in AI
projects, safeguarding against data breaches and misuse.

7.4 The Imperative for Transparency in AI Operations
Need for Transparency: Discuss the importance of transparency in AI operations, particularly in how AI models
make decisions and process inputs.
Enhancing Accountability: Explore approaches to increase accountability in AI applications, including the
implementation of explainability features and audit trails.

7.5 Sustainable AI Development: An Imperative for the Future
Environmental Impact of AI: Address the environmental implications of training extensive AI models, emphasizing
the carbon footprint associated with computational resources.
Promoting Sustainability: Advocate for sustainable practices in AI development and usage to minimize
environmental impact and encourage eco-friendly innovations.

7.6 Ethical Scenario Analysis in AI: Navigating the Complex Landscape
Emerging AI Trends: Examination of upcoming trends and breakthrough technologies in AI, contemplating their
potential societal and technological impacts.

Evolving Role of Prompt Engineering: Discuss how prompt engineering is adapting to and shaping the future
landscape of AI applications, highlighting its growing importance.

7.7 Navigating the Complex Landscape of AI Regulations and Governance
Applying Ethical Principles: Analyze hypothetical scenarios to practice applying ethical principles in AI decision-
making, fostering critical thinking about ethical dilemmas.
Discussion on Ethical Implications: Facilitate group discussions or individual reflections on the ethical
considerations and implications of various AI applications, promoting a deep understanding of ethical challenges.

7.8 Navigating the Regulatory Landscape: A Guide for AI Practitioners
AI Regulations Overview: Provide an overview of existing and forthcoming regulations affecting AI development
and usage worldwide, highlighting key legal frameworks and governance structures.
Regulatory Implications for Practitioners: Discuss the implications of these regulations for AI developers and
prompt engineers, including compliance challenges and opportunities for advocacy.

7.9 Ethical Frameworks and Guidelines in AI Development
Introduction to Ethical Frameworks: Introduce established ethical frameworks and guidelines designed to guide
responsible AI development and usage.
Adopting Ethical Practices: Encourage the integration of these ethical guidelines into AI project development and
professional conduct, aiming to foster a culture of ethical responsibility in the AI community.
AI+ Prompt Engineer Level 1 Detailed Curriculum
Date Issued: 20/01/2024
Version: 1.1