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Overview

In this lesson, you will:

• Apply all foundational prompt engineering techniques in a single project.
• Practice Chain-of-Thought (CoT), ReAct, Prompt Chaining, Templates, and Output Control together.
• Learn how to design a multi-step AI workflow for real-world tasks.
• Build confidence in practical AI interaction.

By the end, you will be able to create a structured prompt workflow that solves complex tasks reliably.

Key Concepts

• Integrative Prompting: Combining multiple techniques for a single task.
• Multi-Step Workflow: Breaking tasks into reasoning, acting, and formatting steps.
• Templates + Output Control: Using placeholders, temperature, max tokens, and system messages.
• Practical Confidence: Applying foundational skills in realistic scenarios.

Practical Approach

Step 1: Pick a Real-World Task

Choose a task that requires multiple steps, such as:

• Summarizing a long article and extracting action items.
• Analyzing a dataset and generating insights.
• Writing a short report with recommendations.
• Coding a small function with explanations.

Step 2: Design Your Prompt Workflow

1. Use CoT: Ask AI to reason step by step.
2. Use ReAct: Have AI perform actions after reasoning.
3. Chain Prompts: Break complex tasks into 2–3 sub-tasks.
4. Templates: Create reusable structures for each sub-task.
5. Control Output: Set temperature, max tokens, and system messages.

Step 3: Execute the Workflow

• Run each prompt sequentially.
• Feed outputs from one prompt into the next if chaining.
• Adjust parameters (temperature, tokens) for best clarity and style.

Practical Example

Task: Summarize an article, extract key points, and create a to-do list for action items.

Workflow:

1. Prompt 1 – Summarize:

Role: You are a content editor.
Task: Summarize this article in 3 concise bullet points.
Input: {Insert article text}

2. Prompt 2 – Extract Action Items:

Role: You are a personal assistant.
Task: From the summary above, extract 3 action items.

3. Prompt 3 – Format Output:

Role: You are a professional assistant.
Task: Create a numbered to-do list from the action items.

Parameters:

• Temperature: 0.3 for accuracy, 0.7 for creativity
• Max tokens: 150–200 depending on task length
• System messages: Friendly and professional tone

Tools for Hands-On Practice

• ChatGPT / OpenAI Playground: Test multi-step workflows.
• Replit / Jupyter Notebook: Automate workflow chaining.
• Notion AI / Dify: Test templates and multi-step tasks interactively.

Step-by-Step Beginner Activity

1. Select an article, email, or dataset.
2. Design a 3-step prompt workflow: summarize → extract → format.
3. Use CoT for reasoning in each step.
4. Apply ReAct for actions (e.g., create lists, code, recommendations).
5. Test different temperature and max token settings.
6. Review results and refine prompts iteratively.

Exercises

1. Summarize a news article, extract 3 action items, and format a task list.
2. Analyze a short dataset, provide stepwise insights, and generate recommendations.
3. Write a short Python function with CoT reasoning, explanation, and final code.
4. Experiment with parameters to optimize output quality and clarity.

Summary & Key Takeaways

• Combining all foundational techniques prepares learners for real-world AI tasks.
• Multi-step workflows using CoT, ReAct, chaining, templates, and output control increase accuracy and consistency.
• Hands-on practice builds confidence in practical AI interaction.
• This project bridges Foundations with the Builder Zone, where learners apply prompt engineering in automation, productivity, and creative workflows.