In this lesson, you will learn how to use large language models (LLMs) and Python to solve everyday work tasks, even if you do not have a technical background. Many people face repetitive or time-consuming tasks at work, such as transforming data, extracting information, or summarizing results. LLMs can help you write simple scripts to automate these tasks, making your work more efficient.

By the end of this lesson, you will see how to use LLMs to generate Python code for real-world problems. You will also learn how to structure your prompts so the model gives you exactly what you need.

Python is one of the most popular programming languages for automating tasks. There are a few reasons for this:

• Readability: Python code is easy to read and understand, even for beginners.
• Versatility: Python can handle many types of data, such as text, tables, and files.
• Community Support: There are many libraries and resources available for almost any task you can imagine.

For example, if you need to process a spreadsheet, extract information from a document, or convert data from one format to another, Python is a great choice. LLMs can help you write the code you need, even if you are not a programmer.

Let’s walk through a practical example step by step. Imagine you have an HTML page with a table listing Beethoven’s most important pieces. You want to turn this table into a JSON file so you can use the data elsewhere.

When working with LLMs, describing your task in detail is important. Here is an example of a prompt:

By specifying the language (Python), the input (HTML table), and the output (JSON file), you help the LLM generate exactly the code you need.

Let’s look at two more examples of using Python and LLMs to automate everyday tasks.

Suppose you have a text file with a list of contacts, and you want to extract all the email addresses. You can ask the LLM:

Explanation:
This prompt tells the LLM to focus on extracting and saving email addresses in a simple format. The generated script will likely use a complex programming technique called "regular expressions" to find emails and write them to a new file. This complex task requires a lot of knowledge, but it is also a standard task, so LLMs handle it very well.

Imagine you have a CSV file with survey responses and want to count how many people answered "Yes" or "No" to a question. You can prompt the LLM like this:

Explanation:
This prompt asks for a script that reads the CSV, counts the responses, and prints the results. The LLM will generate code that uses Python’s csv module to process the file and count the answers.

These examples show how you can use LLMs to quickly generate scripts for everyday tasks, even if you are not a programmer.

While LLMs can help you generate runnable Python code, you still need a correctly set up environment to actually run the scripts. This means having Python installed on your computer and any additional tools or libraries the code might require. If you are unsure how to set up your environment, you can ask the LLM for step-by-step instructions or look for beginner guides online.

Python uses libraries (also called modules or packages) to add extra features, such as reading Excel files or working with web data. If you try to run a script and see an error like ModuleNotFoundError: No module named 'some_library', it means the code is using a library that is not installed on your system.

You have two main options:

• Ask the LLM to avoid using this module: You can prompt the LLM to rewrite the code without using the missing library or to use only standard Python modules.
• Install the module: You can install the required library using the terminal command pip install <modulename>. If you are unsure how to do this, you can ask the LLM for detailed instructions.

This way, you can resolve most environmental issues and run your scripts smoothly.

While LLMs and Python can help automate many everyday tasks, there are significant limitations to keep in mind—especially if you are not a technical user:

• Security Risks: LLMs may generate code that is not secure. For example, scripts that download files from the internet or process sensitive data could expose you to security threats if you do not fully understand what the code is doing.
• Data Privacy: If your data contains confidential or personal information, be careful not to share it with online LLM tools, as your data might be stored or used to train future models.
• Code Quality: LLM-generated code may work for simple tasks, but it can have bugs or edge cases that are not appropriately handled. Always test the code with your data before using it in important workflows.
• Complex Tasks: For complex automation, such as integrating with company systems, handling large datasets, or building user interfaces, LLMs may not generate reliable or maintainable code. In these cases, it is better to consult with a professional developer.
• Maintenance: If something goes wrong or you need to update the script later, you may need technical knowledge to troubleshoot or modify the code.

In summary: Use LLMs for simple, well-defined tasks where mistakes are not costly. Avoid using LLM-generated code for critical, sensitive, or complex workflows unless you have technical support. Always review and test any code before using it in your work.

In this lesson, you learned how to use LLMs and Python to tackle standard programming tasks in everyday work. You saw how to write clear prompts that describe your task, specify the output format, and get usable code for real-world problems. You also explored examples like parsing tables, extracting emails, and summarizing survey results.

You have now reached the end of this course. Well done! You are ready to try these techniques in practice and use LLMs to make your work easier and more efficient. Good luck, and experiment with new ways to automate your daily tasks!