ChatGPT o1-preview excels at code generation | InfoWorld
When the OpenAI o1 models were released, my initial reaction was one of indifference. I assumed it would be just another run-of-the-mill AI model. However, upon testing the o1-preview model, specifically on a standard programming task, I was pleasantly surprised by its capabilities.
The ChatGPT o1-preview model, touted as an early version of the highly anticipated “Strawberry” model, surpassed my expectations. I decided to challenge it by requesting the implementation of a QuickSort algorithm as a C++ function with integer inputs—a task that had proven challenging for previous models. To my delight, the model's initial response was not only correct but also came with detailed explanations of its processes. Unsatisfied with the initial output, I iterated with the model, pushing for further improvements.
Testing the QuickSort Functionality
After multiple interactions and a minor model crash that the o1-preview model managed to recover from, I finally obtained a robust and optimized QuickSort function in modern C++. The final implementation included features such as randomized pivot selection, gated insertion sorting for small runs, Dutch-flag three-way partitioning for handling duplicates, exception handling, parallelization using threads for large arrays, and a comprehensive test suite.
The end result was akin to the work of a proficient junior C++ programmer well-versed in sorting algorithms. In total, the ChatGPT o1-preview model generated five progressively enhanced versions of the QuickSort algorithm in C++.
Crafting QuickSort
Initially, I tasked the model with creating a C++ function for QuickSort on integers along with a test suite to evaluate its accuracy.
Enhancing the QuickSort Algorithm
To overcome the worst-case performance scenario of QuickSort, I collaborated with the o1-preview model to introduce randomized pivot selection. This modification aimed to improve the algorithm's efficiency, especially under adverse conditions, and included a detailed test suite for validation purposes.
Timing the QuickSort Function
Realizing the importance of assessing the performance of the QuickSort function, I updated the code to include timing measurements for each test case using the chrono library. The timing focused solely on the sorting operation and excluded setup or teardown activities to ensure accurate results.
Concluding Thoughts
The ChatGPT o1-preview model showcased remarkable prowess in code generation, particularly in crafting and optimizing the QuickSort algorithm in C++. By incorporating features such as randomized pivot selection and precise timing measurements, the model demonstrated its ability to generate efficient and reliable code for diverse programming tasks.
