Description

Introduction
As AI tools like ChatGPT and large language models become embedded in software development and data analysis workflows, prompt engineering is now a vital skill. This course equips developers and analysts with targeted strategies to write effective prompts that streamline coding, automate tasks, debug errors, generate queries, and enhance analytical output.

Prerequisites
Basic knowledge of programming or scripting languages (e.g., Python, SQL)
Familiarity with development or analytics tools and workflows
No prior experience with AI or prompt engineering required

Table of Contents

1. Introduction to Prompt Engineering for Technical Roles
   1.1 Role of LLMs in Software and Data Workflows
   1.2 Prompt Engineering: A Tool for Developers and Analysts
   1.3 Overview of Common Use Cases

2. Prompt Fundamentals: Structuring for Precision
   2.1 Clear Instructions and Desired Outputs
   2.2 Role Assignment and Context Framing
   2.3 Formatting for Consistency and Readability

3. Coding with Prompts
   3.1 Generating Code Snippets from Natural Language
   3.2 Debugging and Explaining Code via Prompts
   3.3 Using Prompts to Refactor or Optimize Code

4. Data Analysis and Visualization Prompts
   4.1 Creating SQL Queries from Business Questions
   4.2 Prompting for Data Cleaning Scripts
   4.3 Visualizing Results with Prompt-Driven Tools

5. Prompting for Documentation and Explanation
   5.1 Auto-generating Technical Documentation
   5.2 Creating Summaries and Code Comments
   5.3 Explaining Complex Logic or Algorithms

6. Automating Repetitive Tasks
   6.1 Prompts for Writing Shell Scripts or Batch Jobs
   6.2 Template Creation and Reuse Strategies
   6.3 Prompt Automation in DevOps and BI Tools

7. Building Prompt Libraries for Team Use
   7.1 Categorizing Prompts by Task and Domain
   7.2 Version Control and Testing of Prompts
   7.3 Integrating Prompt Kits into Toolchains

8. Advanced Prompting Strategies
   8.1 Chain-of-Thought and Step-by-Step Prompts
   8.2 Few-shot Prompting for Consistent Output
   8.3 Leveraging Prompt Memory in Chat Interfaces

9. Common Pitfalls and Optimization
   9.1 Avoiding Overgeneralization and Vagueness
   9.2 Reducing Hallucinated or Incorrect Output
   9.3 Iterating on Prompts for Better Accuracy

10. Ethical Use of Prompts in Development and Analysis
    10.1 Addressing Data Sensitivity and Privacy
    10.2 Avoiding Bias in AI-Generated Code or Analysis
    10.3 Verification and Human-in-the-Loop Practices

Mastering prompt strategies empowers developers and analysts to use AI as a smart assistant—helping automate, accelerate, and improve daily technical workflows. From writing scripts to querying data or generating documentation, strong prompt design helps deliver higher-quality results, more efficiently. This skill is now an essential part of modern technical toolkits.