Using Multi-Step Prompts to Automatically Generate Python Unit Test Code (Development of Large Model Applications 8)

4 Steps to Automatically Generate Python Unit Test Code with AI

Hello everyone, welcome to the "Development of Large Model Applications" column.

In the Era of Large Model Applications, Everyone Can Be a Programmer (Development of large model applications 1)

Order Management Using OpenAI Assistants' Functions(Development of large model applications 2)

Thread and Run State Analysis in OpenAI Assistants(Development of large model applications 3)

Using Code Interpreter in Assistants for Data Analysis(Development of large model applications 4)

Using the File Search (RAG) Tool in Assistants for Knowledge Retrieval(Development of large model applications 5)

5 Essential Prompt Engineering Tips for AI Model Mastery(Development of large model applications 6)

5 Frameworks to Guide Better Reasoning in Models (Development of Large Model Applications 7)

In the last lesson, we learned some basic principles and techniques of prompt engineering, such as writing clear instructions, providing reference materials, divide-and-conquer strategies, multi-angle thinking, and using external tools.

We also introduced the design of thinking frameworks to guide large models in deep thinking.

These methods help us design high-quality prompts and fully utilize the potential of language models.

Now, we enter the most substantial part of this course: solving practical problems.

This section focuses on "how to use large language models and natural language programming to solve real-world problems."

In this lesson, we will start with simple applications, introducing how to use prompt engineering techniques to guide large language models to automatically generate Python unit test code.