Welcome to Module 5 — Prompt Engineering & Context Engineering.

In this lesson, you will learn how to communicate effectively with AI models and design prompts that produce accurate, relevant, and consistent results. You will move beyond simple questions and learn how instructions, context, examples, constraints, tools, and structured inputs can be combined to create reliable AI workflows.

Topics Covered:

1. What is Prompt Engineering?
   Understand prompt engineering and why the way instructions are written can significantly influence AI model outputs.
2. Anatomy of an Effective Prompt
   Learn the key components of a strong prompt, including:
   • Role
   • Task
   • Context
   • Instructions
   • Constraints
   • Examples
   • Expected Output
3. Zero-Shot Prompting
   Learn how to ask an AI model to perform a task without providing examples.
4. One-Shot and Few-Shot Prompting
   Understand how examples can guide an AI model toward a specific task, format, or style.
5. Role and System Instructions
   Learn how role-based instructions and system-level guidance can establish the behavior, purpose, and boundaries of an AI assistant.
6. Context Engineering
   Understand how relevant information, instructions, history, documents, and other inputs can be organized to provide AI models with the context required to perform a task effectively.
7. Instructions, Context, and Constraints
   Learn how to clearly define what the AI should do, what information it should use, and what limitations it must follow.
8. Structured Outputs
   Learn how to guide AI models to produce structured responses such as:
   • JSON
   • Tables
   • Lists
   • Templates
   • Standardized formats
9. Prompt Templates
   Understand how reusable prompt structures can improve consistency and make AI workflows easier to maintain.
10. Prompt Chaining
    Learn how multiple prompts can be connected together to complete complex tasks step by step.
11. Reasoning and Task Decomposition
    Understand how complex tasks can be broken into smaller, well-defined steps to improve the quality and reliability of AI workflows.
12. ReAct and Tool-Based Reasoning
    Explore how AI systems can combine reasoning with actions and external tools to accomplish tasks.
13. Reflection and Self-Critique
    Learn how AI workflows can use review and refinement steps to identify errors and improve generated outputs.
14. Prompt Optimization and Evaluation
    Learn how to test prompts, compare outputs, identify weaknesses, and continuously improve prompt performance.
15. Prompt Injection and Jailbreaks
    Understand common risks associated with malicious or conflicting instructions and learn the importance of designing safer AI workflows.
16. Advanced Context Engineering
    Explore how context can be selected, structured, prioritized, and managed when working with complex AI applications and long conversations.
17. Real-World Applications
    Learn how prompt and context engineering are used for AI assistants, content generation, research, coding, customer support, automation, data analysis, and business workflows.

Mini Project:
Create a professional AI Prompt Library containing reusable prompts for multiple tasks such as content creation, research, data analysis, coding, customer support, and business automation. Build and test an AI assistant using these prompts and structured context.

Learning Outcome:
By the end of this lesson, you will be able to design effective prompts, structure useful context, control AI outputs, create reusable prompt templates, build multi-step AI workflows, evaluate responses, and identify common prompt security risks.

Lesson Resources:
Video Lesson + Presentation PDF + Quize