AI+ Prompt Engineer Level 2

Description

AI+ Prompt Engineer Level 2 (5 Days)

Program Detailed Curriculum

Executive Summary
The AI+ Prompt Engineer Level 2 certification is designed for developers aiming to master advanced
prompt engineering techniques. This program equips learners with skills in crafting and optimizing
prompts, integrating them with cutting-edge development tools, and applying them across diverse
domains. Through project-based learning, participants gain practical experience by working on real-world
AI projects. The curriculum explores advanced strategies, experimentation, and optimization, ensuring
proficiency in creating impactful AI-driven solutions. Ideal for professionals looking to enhance their
expertise, this certification bridges the gap between AI innovation and application, empowering
developers to lead in the evolving field of prompt engineering.

Course Prerequisites
Familiarity with at least one programming language (Python recommended).
Basic knowledge of RESTful services and API interactions.
Basic understanding of AI concepts and language models.

Module 1

Introduction to Prompt Engineering for Developers

1.1 Overview of Prompt Engineering
Prompt Engineering in Software Development: Definition and significance of prompt engineering in optimizing AI
model outputs within software projects.
Applications of Prompt Engineering in AI Projects: Techniques for applying prompt engineering across AI projects
to improve task-specific model performance.

1.2 Basics of API Interaction
API Requests and Responses in Different Languages: Techniques for handling API requests and responses in
Python and JavaScript.
Making Requests to OpenAI’s API: Setting up and making API requests to OpenAI using SDKs and libraries.

1.3 Understanding Prompt Structures
Basic Prompt Syntax and Components: Overview of prompt syntax and key components for developers.
Crafting Prompts for Development Tasks: Techniques for crafting simple prompts for tasks like code completion,
debugging, and testing.

1.4 Case Studies and Best Practices
Examples of Successful Prompt Engineering: Real-world examples of prompt engineering success in software
development.
Avoiding Common Pitfalls in Prompt Engineering: Analysis of common prompt engineering pitfalls and strategies
to avoid them in development.

1.5 Hands-on Exercise
Setting up development environment: Setting up your development environment and making basic API calls with
code examples.

Module 2

Advanced Prompt Design and Engineering

2.1 Designing Advanced Prompt Techniques
Crafting Precise and Impactful Prompts: Techniques for creating clear and effective prompts.
Designing Prompts for Complex Tasks with LangChain: Strategies for designing prompts for multi-step reasoning
tasks using LangChain.
Dynamic Prompt Adjustments: Methods for real-time adaptation of prompts based on user feedback.

2.2 Designing Multi-Turn Interactions
Context Preservation in Conversational AI: Implementing techniques to maintain context in conversational AI
within software applications.
Memory Management for Long Interactions: Methods for handling memory in code to manage extended
interactions programmatically.
Managing Interruptions and Unexpected Inputs: Strategies for managing interruptions and unexpected inputs in
code.

2.3 Contextual and Conditional Prompting
Designing Contextual Prompts for Complex Applications: Techniques for creating prompts that provide nuanced
understanding in complex software applications.
Implementing Conditional Logic in Prompts: Using programming constructs to add conditional logic to prompts.
Strategies for Multi-Modal Prompts: Approaches for integrating text, image, and audio prompts in software
solutions.

2.4 Crafting Domain-Specific Prompts
Embedding Domain Knowledge into Prompts: Techniques for programmatically embedding domain-specific
knowledge into prompts to enhance the accuracy and relevance of AI responses.
Leveraging External APIs and Databases: Techniques for integrating external APIs and databases into your code.
Tailoring Prompts for Specialized Industries: Customizing prompts for specific industries (e.g., healthcare, finance,
law) with code examples.

2.5 Contextual and Stateful Prompt Engineering
Context-Aware Prompts: Techniques for maintaining and utilizing conversation context in prompts.

Adaptive Context-Aware Prompts: Designing prompts that adjust to the evolving flow of conversation.
Leveraging Memory and State: Implementing memory mechanisms to retain context across sessions.

2.6 Meta-Prompting and Autonomous Refinement
Meta-Prompting in Advanced Prompt Engineering: Concept and benefits of meta-prompting for advanced
prompt engineering.
Enhancing Model Adaptability and Intelligence: Techniques to improve model adaptability and intelligence.
Autonomous Prompt Refinement with LangChain: Using LangChain to automatically refine and optimize
prompts.
Successful Meta-Prompting Applications: Examples of effective meta-prompting for autonomous prompt
refinement.

2.7 Hands-on Exercise
Building Advanced LangChain Applications: Create complex applications using advanced prompt techniques.

Module 3

Experimentation and Optimization

3.1 Automated Prompt Optimization Tools
Hyperparameter Optimization for Prompt Tuning: Utilizing hyperparameter optimization techniques to fine-tune
prompts in your code.
AI Tools and Libraries for Prompt Analysis: Leveraging AI tools and libraries to analyze and improve prompts.
Metrics for Prompt Effectiveness: Understanding and implementing metrics to assess prompt effectiveness
programmatically.

3.2 A/B Testing and Evaluation
Designing Experiments for Prompt Variations: Creating rigorous experiments to test different prompt variations in
software applications.
Evaluating Performance with Statistical Methods: Using statistical methods and coding techniques to assess
prompt performance.
Interpreting Results and Drawing Insights: Interpreting experimental results to extract actionable insights from
data.

3.3 Reinforcement Learning for Prompt Engineering
Integrating RL Techniques for Prompt Optimization: Applying reinforcement learning (RL) techniques to enhance
prompt responses in software.
Implementing Real-Time Feedback Loops: Setting up feedback loops for real-time learning and adaptation in your
applications.
Balancing Exploration and Exploitation in Prompts: Balancing exploration and exploitation in prompt design with
code examples.

Module 4

Designing Advanced Strategies for Prompt Engineering

4.1 Contextual and Role-Based Prompting
Preserving Context in Multi-Turn Conversations: Techniques for maintaining context in multi-turn conversations
within applications.
Leveraging Historical Data for Consistency: Using historical data to guide consistent model responses
programmatically.
Avoiding Conversational Drift: Strategies for preventing conversational drift and improving relevance through
code.
Creating Personas for Model Tone and Style: Designing personas to influence the model’s tone and style in
applications.
Role-Based Prompting for Domain-Specific Applications: Using role-based prompting for specialized domains
with code examples.
Role Adaptation Examples: Illustrations of role adaptation in customer service and storytelling applications.

4.2 Adaptive and Multimodal Prompting
Real-Time Prompt Adaptation: Techniques for adjusting prompts in real-time based on user feedback.
Implementing Feedback Loops for Prompt Refinement: Setting up user feedback loops to enhance prompt
effectiveness in software.
Adaptive Systems for Evolving Interactions: Developing adaptive systems to evolve model interactions in
applications.
Designing Multimodal Prompts: Creating prompts that integrate text, images, and audio with code examples.
Role-Based Prompting for Domain-Specific Applications: Using role-based prompting for specialized domains
with code examples.
Practical Multimodal Applications: Examples of applying multimodal prompts in image captioning, audio analysis,
and more.
Seamless Interactions Across Data Types: Strategies for creating smooth interactions involving various data types.
Case Studies in Multimodal Applications: Exploring multimodal applications in healthcare, finance, and
entertainment through case studies.
Challenges and Opportunities in Multimodal Prompt Engineering: Identifying challenges and opportunities in
multimodal prompt engineering.

Module 5

Integration with Development Tools

5.1 Integrating with Popular Development Tools for Prompt Engineering
Integrating Prompt Engineering with VSCode and Jupyter Notebooks: Techniques for incorporating prompt
engineering into VSCode and Jupyter Notebooks environments.
Examples and Workflows for Development Tools: Workflows and examples for seamless integration of prompt
engineering with development tools.

5.2 Code Repositories and Templates for Prompt Engineering
Access to Code Repositories: Providing access to code repositories with examples and templates.
Best Practices for Code Repositories: Guidelines for effectively utilizing and contributing to code repositories.

5.3 Developer Communities and Forums for Prompt Engineering
Participation in Developer Communities: Encouraging engagement in developer communities and forums.
Sharing Knowledge and Best Practices: Promoting the sharing of knowledge and best practices within
communities.

5.4 Version Control in Prompt Engineering Projects
Version Control for Prompt Engineering Projects: Utilizing version control tools like Git to manage prompt
engineering projects.
Collaboration and Project Management with Version Control: Best practices for collaboration and project
management using version control systems.

Module 6

Applications of Prompt Engineering in Various Domains

6.1 Natural Language Processing (NLP) Applications using Prompt Engineering
Prompts for Automated Text Generation: Designing prompts for automated text generation and creative writing
in software projects.
Summarizing Complex Documents Programmatically: Techniques for summarizing complex documents and
extracting key insights programmatically.
Sentiment Analysis and Opinion Mining: Implementing sentiment analysis to assess emotional tone and conduct
opinion mining in applications.
Case Study: Developing a news summarization tool using advanced prompt techniques.
Designing Conversational Agents: Creating conversational agents for a variety of applications.
Role-Based Prompts for Virtual Assistant Personas: Using role-based prompts to customize virtual assistant
personas.
Case Study: Developing a news summarization tool using advanced prompt techniques.

6.2 Business Applications using Prompt Engineering
Automating Customer Inquiries with Prompts: Using prompts to automate common customer inquiries and
support tasks through code.
Integrating AI with Business Workflows: Incorporating AI-driven systems into existing business workflows.
Case Study: Implementing a smart customer support system for an e-commerce platform.
Designing Prompts for Business Data Insights: Creating prompts to extract actionable insights from business data
in software.
Automating Reporting and Analytics with Prompts: Using customized prompts to automate reporting and
analytics tasks.
Case Study: Using prompt engineering for real-time sales forecasting.

6.3 Creative Applications using Prompt Engineering
Prompts for Generating Stories and Narratives: Crafting prompts to generate compelling stories and narratives in
software projects.
User-Driven Content Customization: Implementing content customization and personalization based on user
input.
Case Study: Building an AI-assisted creative writing tool for authors.
Prompts for Dynamic Gameplay Experiences: Using prompts to create dynamic and interactive gameplay
experiences in games.
AI-Driven Content Generation in Multimedia: Techniques for integrating AI-driven content generation into
multimedia projects.
Case Study: Developing an AI-driven interactive storytelling platform.
Text Summarization: Programmatically reduce a lengthy text into a concise summary, capturing key information
and main points for easier understanding.
Language Translation: Using code to translate text between languages, enabling cross-language communication
and understanding in various applications.
Creative Writing: Utilize prompts to generate creative content, story ideas, or dialogue, fostering innovation in
writing projects through AI assistance.
Question Answering: Design prompts to extract accurate answers from a given context, enabling efficient
information retrieval in applications like chatbots or research tools.

Module 7

Project-Based Learning: Real-World AI Projects using prompt Engineering

7.1 Project 1: AI-Driven Customer Support
AI-Driven Customer Support: Develop an advanced conversational AI bot for customer support using prompt
engineering techniques in code.

7.2 Project 2: Personalized Content Generation
Personalized Content Generation: Create an AI-powered content generation tool using advanced prompt
engineering for personalized marketing content in software.

7.3 Project 3: AI in Data Analysis
AI in Data Analysis: Implement AI solutions for data analysis and provide actionable insights using prompt
engineering techniques in your code.
AI+ Prompt Engineer Level 2 Detailed Curriculum
Date Issued: 20/01/2024
Version: 1.1