It’s no secret that ChatGpt has many applications and is growing more useful every day. It’s a blessing that we can have access to such technology at our fingertips. But having access to the technology and using it effectively are two different things. Introducing prompt engineering.

If you dont know what ChatGpt is already please read my intro article: https://guardianreport.co.za/chatgpt-bridging-humans-and-ai/

What is prompt engineering in ChatGPT?

If you are like me and tried to use Chat GPT for writing your business plan so you can attract investors, you will see that what Chat GPT spits out is a generic summary of the structure of what a business plan should look like as opposed to an actual, in-depth, investor-ready business plan. You may think, Oh no, it’s back to the drawing board. However, By providing more specific prompts, users can have more meaningful and accurate interactions with Chatgpt.

How Prompt engineering works in ChatGPT

Prompt engineering has to do with Changing our inputs so we can get better outputs. In simple terms, change the way we ask chatgpt out questions. One of the best ways to get Chatgpt to give us better outputs is through acquisition. That’s a fancy way of assigning the chat bot a personality or persona.

Example of Prompt Engineering in ChatGPT

Say, for instance, we want to create a study plan so we can learn how to code, as we know coders get paid lots of money. By simply asking Chat GPT, this is what it gives us:

Learning coding requires a structured and consistent approach. Here’s a comprehensive study plan to help you learn coding effectively. This plan assumes you have little to no prior experience with coding. Customize the plan based on your specific interests and goals. Remember to stay patient and persistent throughout the learning process.

Step 1: Choose a Programming Language

Start by selecting a programming language to learn. Popular beginner-friendly options include Python, JavaScript, and Ruby. Python is often recommended for its readability and versatility.

Step 2: Set Clear goals.

Define your coding goals. Are you learning to build websites, mobile apps, or data analysis tools? Knowing your objectives will help you stay focused and motivated.

Step 3: Online Courses and Tutorials

Begin with structured online courses or tutorials. Some popular platforms include:

• Codecademy (codecademy.com)
• FreeCodeCamp (freecodecamp.org)
• Coursera (coursera.org)
• Udemy (udemy.com)
• edX (edx.org)

These platforms offer interactive lessons, exercises, and projects to help you practice coding.

Step 4: Books and Documentation

Complement online courses with coding books and official documentation for your chosen language. Books like “Automate the Boring Stuff with Python” and “Eloquent JavaScript” are excellent starting points.

Step 5: Hands-On Practice

Learning to code requires hands-on practice. Start with simple coding challenges and gradually move to more complex projects. Websites like LeetCode (leetcode.com) and HackerRank (hackerrank.com) offer coding challenges.

Step 6: Personal Projects

Apply what you’ve learned by working on personal projects. Choose projects that align with your interests and goals. It could be creating a personal website, building a simple game, or automating a repetitive task.

Step 7: Join Coding Communities

Engage with coding communities like GitHub (github.com) and Stack Overflow (stackoverflow.com). These platforms provide a wealth of knowledge and support from experienced developers.

Step 8: Learn from Others

Follow coding tutorials, webinars, and conferences to learn from experienced programmers. Online platforms like YouTube and Twitch often host live coding sessions and coding workshops.

Step 9: Code Reviews

Participate in code reviews. Share your projects with others, and ask for feedback. Reviewing other people’s code will also help you learn new techniques and best practices.

Step 10: A Consistent Learning Schedule

Create a consistent learning schedule. Set aside dedicated time each day or week to practice coding. Regular practice is crucial for reinforcing your knowledge and skills.

Step 11: Stay Updated

Stay up-to-date with the latest trends, libraries, and frameworks in your chosen programming language. Technology evolves rapidly, so continuous learning is essential.

Step 12: Collaboration

Collaborate with other learners and programmers. Join coding meetups or forums where you can work on group projects and exchange knowledge.

Step 13: Take breaks.

Learning to code can be challenging, and you might encounter roadblocks. Remember to take breaks when needed to prevent burnout. Take walks, meditate, or engage in other activities to recharge your mind.

Step 14: Reflect and Improve

periodically evaluate your progress and identify areas for improvement. Reflect on what you’ve learned and how you can apply it to real-world projects.

Step 15: Keep Building

Coding is a skill that improves with practice. Keep building projects, experimenting, and applying your knowledge to reinforce what you’ve learned.

Remember, learning to code is a journey, not a destination. Be patient, stay curious, and embrace the learning process. Happy coding!

As you can see, this is a very generic output, and it does not take us where we want to be. Now by altering our inputs or prompt engineering, we can give chat gpt something like this:

“Act as a coding tutor that creates study plans to help people learn to code. You will be provided with the goal of the student, their time commitment, and resource preferences. You will create a study plan with timelines and links to resources. Only include relevant resources because time is limited. My first request was, “I want to become a data scientist, but I do not know how to code. I can study 10 hours per week and only want video resources. I want to learn to code in Python. Create a study plan for me.”

Notice how in-depth and specific the prompt is. This is the output.
Great! Let’s create a study plan tailored to your goal of becoming a data scientist, starting with the basics of coding in Python. Given your time commitment of 10 hours per week and preference for video resources, here’s a structured plan:

Week 1: Introduction to Python

• Time: 2 hours
• Resource:
  • Codecademy Python Course: Codecademy offers interactive lessons with video explanations, quizzes, and coding exercises.

Week 2: Python Basics

• Time: 3 hours
• Resource:
  • Python Programming Tutorial for Beginners: This YouTube tutorial by Corey Schafer covers Python basics with clear explanations and practical examples.

Week 3: Data Types and Control Flow

• Time: 2 hours
• Resource:
  • Python Data Types and Control Flow: A Video by Telusko Explaining Data Types, Loops, and Conditional Statements in Python.

Week 4: Functions and File Handling

• Time: 2 hours
• Resource:
  • Python Functions and File Handling: Another video by Telusko, covering functions and reading and writing files in Python.

Week 5: NumPy and Pandas

• Time: 3 hours
• Resource:
  • NumPy Tutorial: This tutorial by Corey Schafer introduces NumPy, a fundamental library for numerical computing in Python.
  • Pandas Tutorial: A comprehensive tutorial by Corey Schafer on Pandas, a powerful data manipulation library in Python.

Week 6: Data Visualization with Matplotlib and Seaborn

• Time: 2 hours
• Resource:
  • Matplotlib Tutorial: This tutorial by Corey Schafer introduces Matplotlib, a popular plotting library in Python.
  • Seaborn Tutorial: Corey Schafer’s tutorial on Seaborn, a library for creating attractive statistical graphics.

Week 7: Introduction to Machine Learning

• Time: 2 hours
• Resource:
  • Introduction to Machine Learning with Python: A Video by sentdexprovidesg an overview of machine learning concepts using Python.

Week 8: Data Science Projects and Practice

• Time: 2 hours
• Resource:
  • Work on your first small data science project. You can find datasets and ideas on platforms like Kaggle (kaggle.com) or DataCamp (datacamp.com).

Week 9–10: Deep Dive into Data Science Libraries

• Time: 4 hours
• Resource:
  • Explore more advanced topics in NumPy, Pandas, Matplotlib, and Seaborn by following specific tutorials on these libraries and experimenting with more complex datasets.

Week 11–12: Capstone Data Science Project

• Time: 4 hours
• Resource:
  • Plan and execute a larger data science project, applying the skills you’ve learned throughout the study plan. Look for inspiration in real-world datasets and try to solve a practical problem.

Ongoing Learning:

• Join online communities and forums, such as Stack Overflow and Reddit, to ask questions and learn from others’ experiences.
• Follow data science experts on platforms like Twitter and LinkedIn to stay updated with the latest trends and techniques in the field.

Remember, consistency and practice are keys to mastering coding and data science. Allocate time each week to practice, work on projects, and reinforce your knowledge. Happy learning on your journey to becoming a data scientist!

Embrace the Magic: Mastering ChatGPT with Prompt Engineering

Unlocking the true potential of ChatGPT is within your grasp through the art of prompt engineering. By giving the language model specific roles and clear instructions, you’ll witness its brilliance come to life. This way of asking the AI works with virtually anything. The trick is to assigned a role. Who, where, what, how and why the more specific the better.