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AI+ Prompt Engineer Level 2™ Spanish
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Introduction
Course Introduction
Module 1: Introduction to Prompt Engineering for Developers
1.1 Definition, Importance of Prompt Engineering in Software Development
1.2 Practical Applications of Prompt Engineering in Various AI Projects
1.3 API Requests and Responses in Different Programming Languages (Python, JavaScript)
1.4 Making Requests to OpenAI’s API
1.5 Basic Prompt Syntax and Components for Developers
1.6 Crafting Prompts for Development Tasks
1.7 Improving Customer Support Chatbots at Zendesk
1.8 Code Generation at GitHub Copilot
1.9 Content Moderation at OpenAI’s GPT-4
1.10 Documentation Generation in Microsoft’s Power Platform
1.11 AI-assisted Game Development at Roblox
1.12 Avoiding Common Pitfalls in Prompt Engineering
Module 2: Advanced Prompt Design and Engineering
2.1 Techniques for crafting precise and impactful prompts
2.2 Designing prompts for complex, multi-step reasoning tasks with LangChain
2.3 DPAs
2.4 Tips and tricks for designing advanced prompt techniques
2.5 Context Preservation In Conversational AI
2.6 Step-by-Step Example
2.7 Memory Management for Handling Long Interactions
2.8 Chunking and Memory Partitioning
2.9 Managing Interruptions and Unexpected Inputs
2.10 Designing Contextual Prompts for Complex Applications
2.11 Implementing conditional logic in prompts using programming constructs
2.12 Strategies for Multi-Modal Prompts
2.13 Embedding Domain Knowledge into Prompts
2.14 Leveraging External APIs and Databases
2.15 Context-Aware Prompts
2.16 Adaptive Context-Aware Prompts
2.17 Leveraging Memory and State
2.18 Meta-Prompting in Advanced Prompt Engineering
2.19 Techniques for enhancing model adaptability and intelligence
2.20 Autonomous Prompt Refinement with LangChain
2.21 Successful Meta-Prompting Applications
Module 3: Experimentation and Optimization
3.1 Hyperparameter Optimization for Prompt Tuning
3.2 AI Tools and Libraries for Prompt Analysis
3.3 Metrics for Prompt Effectiveness
3.5 Designing rigorous experiments to test prompt variations in software applications
3.5 Evaluating Performance With Statistical Methods
3.6 Drawing Actionable Insights
3.7 Integrating RL techniques for Prompt Optimization
3.8 Implementing Real-time Feedback Loops
3.9 Balancing Exploration and Exploitation in Prompts
3.10 Significance of UCB Strategy
Module 4: Designing Advanced Strategies for Prompt Engineering
4.1 Preserving Context in Multi-Turn Conversations Within Applications
4.2 Leveraging Historical Data for Consistency
4.3 Avoiding Conversational Drift
4.4 Creating Personas for Model Tone and Style
4.5 Creating Personas to Influence the Model’s Tone and Style in Applications
4.6 Role-based Prompting for Domain-Specific Applications
4.7 Role Adaptation Examples
4.8 Adaptive and Multimodal Prompting
4.9 Real-time Prompt Adaptation
4.10 Refining Prompts with Feedback Loop
4.11 Seamless Interactions Across Data Types
4.12 Case Studies in Multimodal Applications
4.13 Challenges and opportunities in multimodal prompt engineering
Module 5: Integration with Development Tools
5.1 Integrating Prompt Engineering With VSCode, and Jupyter Notebooks
5.2 Examples and Workflows for Development Tools
5.3 Access To Code Repositories
5.4 Best Practices for Code Repositories
5.5 Participation in Developer Communities
5.6 Sharing Knowledge and Best Practices
5.7 Version Control for Prompt Engineering Projects
5.8 Collaboration and Project Management With Version Control
Module 6: Applications of Prompt Engineering in Various Domains
6.1 Prompts for Automated Text Generation
6.2 Summarizing Complex Documents Programmatically
6.3 Sentiment Analysis and Opinion Mining
6.4 Case Study
6.5 Designing Conversational Agents
6.6 Role-based Prompts for Virtual Assistant Personas
6.7 Case Study
6.8 Automating Customer Inquiries with Prompts
6.9 Integrating AI With Business Workflows
6.10 Case Study
6.11 Designing Prompts for Business Data Insights
6.12 Automating Reporting and Analytics With Prompts
6.13 Case Study
6.14 Prompts for Generating Stories and Narratives
6.15 Implementing user-driven content customization
6.16 Case Study
6.17 Prompts for Dynamic Gameplay Experiences
6.18 AI-driven Content Generation in Multimedia
6.19 Case Study
6.20 Text Summarization
6.21 Language Translation
6.22 Creative Writing
6.23 Question Answering
Module 7: Project-Based Learning: Real-World AI Projects using Prompt Engineering
7.1 AI-Driven Customer Support
7.2 Personalized Content Generation
7.3 AI in Data Analysis
Course Summary
Course Summary
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AI+ Prompt Engineer Level 2™ Spanish
