AI+ Prompt Engineer Level 2 TM Training | CourseMonster

- AI/ML Developers & Engineers

- Technical Product Owners

- Automation Specialists

- Prompt Engineers (Level 1 certified or equivalent experience)

- Software Developers working with AI APIs

- Advanced Prompt Design and Refinement

- Prompt Optimization Strategies

- API & Tool Integration (LangChain, Copilot, etc.)

- Experimentation with AI Behavior

- AI Workflow Automation

- Practical Application in Business Use-Cases

- 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: A foundational introduction to the field of prompt engineering, explaining how it enables developers to get desired, high-quality outputs from generative AI models.
• 1.2 Basics of API Interaction: Learn how to interact with LLMs programmatically using APIs, which is a key skill for integrating AI into software.
• 1.3 Understanding Prompt Structures: Explore the fundamental components of a well-structured prompt, including instructions, context, and desired output format.
• 1.4 Case Studies and Best Practices: Analyze real-world examples of effective and ineffective prompts to understand key principles.
• 1.5 Hands-on Exercise: A practical lab to write and refine your first set of prompts using an LLM API.

Module 2: Advanced Prompt Design and Engineering

• 2.1 Designing Advanced Prompt Techniques: A deep dive into advanced strategies like Chain-of-Thought (CoT) prompting, few-shot prompting, and zero-shot CoT to improve model reasoning and accuracy.
• 2.2 Designing Multi-Turn Interactions: Learn how to create prompts that maintain context and coherence over an extended conversation.
• 2.3 Contextual and Conditional Prompting: Master the use of contextual information to guide the model's responses and create prompts that adapt based on specific conditions.
• 2.4 Crafting Domain-Specific Prompts: Learn to tailor prompts to specific industries or technical fields, such as legal, medical, or coding.
• 2.5 Contextual and Stateful Prompt Engineering: A detailed exploration of how to manage conversational state and provide relevant context over time for more sophisticated applications.
• 2.6 Meta-Prompting and Autonomous Refinement: Learn to use one LLM to generate or refine prompts for another, a technique that can lead to more optimal and self-correcting AI systems.
• 2.7 Hands-on Exercise: A practical lab to implement advanced prompting techniques and observe their impact on model output.

Module 3: Experimentation and Optimization

• 3.1 Automated Prompt Optimization Tools: An introduction to tools and frameworks that help automate the process of finding the best prompts for a given task.
• 3.2 A/B Testing and Evaluation: Learn how to systematically test different prompts to determine which ones perform best and how to measure the quality of AI-generated responses.
• 3.3 Reinforcement Learning for Prompt Engineering: Explore the use of reinforcement learning to train models to produce better responses based on human feedback or specific metrics.

Module 4: Designing Advanced Strategies for Prompt Engineering

• 4.1 Contextual and Role-Based Prompting: Learn to assign a specific persona or role to the AI (e.g., "Act as a cybersecurity expert") to ensure its responses are accurate, relevant, and in the correct tone.
• 4.2 Adaptive and Multimodal Prompting: An introduction to creating prompts that can handle various types of input, including text, code, and images, and adapt their responses accordingly.

Module 5: Integration with Development Tools

• 5.1 Integrating with Popular Development Tools for Prompt Engineering: A guide to integrating LLM APIs and prompt engineering workflows with popular development tools and IDEs.
• 5.2 Code Repositories and Templates for Prompt Engineering: Learn to create and manage repositories of effective prompts for various use cases, a key practice for large-scale AI projects.
• 5.3 Developer Communities and Forums for Prompt Engineering: A guide to leveraging online communities, forums, and platforms to share knowledge, collaborate, and stay up-to-date on the latest prompt engineering techniques.
• 5.4 Version Control in Prompt Engineering Projects: Best practices for using version control systems like Git to manage and track changes to your prompts.

Module 6: Applications of Prompt Engineering in Various Domains

• 6.1 Natural Language Processing (NLP) Applications using Prompt Engineering: Explore how prompt engineering can enhance traditional NLP tasks like sentiment analysis, text classification, and summarization.
• 6.2 Business Applications using Prompt Engineering: Case studies on using prompt engineering for tasks such as automated customer support, marketing copy generation, and data analysis.
• 6.3 Creative Applications using Prompt Engineering: A look at how prompt engineering is used in creative fields to generate ideas, write scripts, and create art.

Module 7: Project-Based Learning: Real-World AI Projects Using Prompt Engineering

• 7.1 Project 1: AI-Driven Customer Support: Build a multi-turn chatbot that can handle customer queries, provide a knowledge base, and escalate complex issues.
• 7.2 Project 2: Personalized Content Generation: Create a system that generates personalized content (e.g., product descriptions, blog posts) for a specific audience.
• 7.3 Project 3: AI in Data Analysis: Develop a tool that uses prompt engineering to interpret and summarize data, providing actionable insights for business users.

Optional Module: AI Agents for Prompt Engineering

• 1. What Are AI Agents: A deep dive into the concept of autonomous AI systems that can perceive their environment, plan, and perform a series of actions to achieve a goal.
• 2. Applications and Trends of AI Agents for Prompt Engineers: Explore how AI agents can automate the prompt engineering process itself by generating, testing, and optimizing prompts without human intervention.
• 3. Importance of AI Agents: Understand why AI agents are the next evolution in prompt engineering, enabling more complex, multi-step tasks and reducing the need for constant manual prompting.
• 4. Types of AI Agents: A survey of different types of AI agents, from simple reflex agents to more sophisticated planning and utility-based agents.