In this article, I will be discussing the positives and negatives that I have noticed while working with GPT-4 and generating a fairly simple web application. I will also talk about the three categories: accuracy, speed, and quality, and provide a summary at the end.
Accuracy: Overall, the generated code was accurate and resolved most issues. However, there were some quality issues. For example, longer threads produced more inaccuracies. It is important to have domain knowledge to ask the right questions and avoid confusion.
Speed: Lazy loading decreased content generation time, but it required further querying, which increased overall time. Skipping pre and post explanations saved time, as GPT-4 works best on code generation. The best case scenario is fully generated and accurate code, while the worst case scenario is inaccurate code with lazy loading.
Quality: There were some quality issues that we noticed. The DB context was created with more connections than necessary, and the book servers didn’t get rid of old books with no reviews immediately. The orders controller and reviews controller also had performance issues due to querying the database each time.
Miscellaneous Notes: Configurations are difficult to generate and are better written manually. Reviewing the results takes time, and probabilistic nature creates non-deterministic results. The deeper the thread goes, the more mistakes happen, making it harder to follow. It is important to calculate if using GPT-4 is worth it compared to writing code manually.
In conclusion, GPT-4 is fantastic for idea generation and quick lookups, but it requires careful review and consideration. It may improve in future versions, and I will continue to post unbiased case studies. I hope you have enjoyed this series and learned something. Thank you!
