Prompt Engineering Best practices

Table of Contents

1. Challenge

   Step 1: Getting Started

   Welcome to the Prompt Engineering Code Lab! In this first step, you'll set up your Jupyter Notebook, which will be your workspace for the entire lab. You'll import the necessary helper code, including a mock AI model and the sample data we'll be working with. This initial setup is crucial for the hands-on exercises in the following steps.

2. Challenge

   Step 2: Foundational Prompting Styles

   Now that the environment is ready, let's explore the two most fundamental prompting styles: zero-shot and few-shot. Understanding the difference between simply asking for something (zero-shot) and showing the model what you want with examples (few-shot) is the first major step toward becoming a skilled prompt engineer. We will apply these techniques to perform basic analysis on our customer feedback data.

3. Challenge

   Step 3: Advanced Prompting Techniques

   With the basics covered, we can move on to more advanced and powerful techniques. In this step, you'll learn how to assign a 'persona' to the model with role-based prompting to control its tone and style. You'll also learn about Chain-of-Thought (CoT) prompting, a method to guide the model through complex reasoning tasks by asking it to 'think step by step'.

4. Challenge

   Step 4: Analyzing Unstructured Data

   This is where prompt engineering shows its true power. We'll use the techniques you've learned to perform a real-world task: analyzing a block of unstructured customer feedback. You will guide the AI to first summarize the text into key points and then to synthesize it further to extract actionable insights that a product team could use.

5. Challenge

   Step 5: Evaluating and Iterating on AI Responses

   Getting a response from an LLM is easy; getting a good response is harder. In this final step, you'll focus on the critical skill of evaluation. You'll learn to spot and document AI 'hallucinations', use multi-turn conversations to refine a vague answer, and even ask the model to evaluate its own performance.