How can we use AI to make software development more efficient and effective?

Take a look at Github Co-Pilot - helps tremendously with 30% efficiency gains

Build strong prompts! Give a lot of context ! And train a model on these data’s retrain it every X ! I have my own fine tune for coding is a game changer.

AI has the potential to revolutionize the way that software is developed. By automating tasks, providing insights, and helping developers write better code, AI can make software development more efficient, effective, and reliable.

Avoid directly asking for the code. Try to break the prompt into several parts, focus on logic and verify with other sources as well. Imagine using AI like hiring an intern with Google search access who can solve a problem for you provided you give specific context to it and you are ready to verify and correct it's mistakes as well. AI will one day evolve where it would perform more than an Intern but till now, this is it. For best auto-completion of code writing, do use Github Copilot. After some time of it's usage, it would adapt to you and it will feel like you are writing syntax in your head and it's automatically being understood by the editor.

Yes, we developed a new agent to automatically do research for different company, it can also used for the software development!

AI can already write small pieces of code. And this greatly simplifies development.

Quick bug fixes without hoping on stackoverflow 😅

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Take it easy. Ask for your followers feedback, and make a list of the drawbacks they mention ( if there are any).

AI can already write small pieces of code.

Hi! I'm a developer with some coding experience but always room for improvement. With ChatGPT's help, I developed and launched GPTBLOX, a Chrome extension, in less than two weeks—something I couldn't have done solo in months. We're at nearly 1,000 users and counting! 🚀 I just launched a new version today: GPTBLOX - ChatGPT/Bard/Claude Save Data Working with ChatGPT was a unique experience, but not without its learning curve. Here are some strategies we employed. • Step-by-Step Output Let ChatGPT write code in files or modules according to steps. For example, first determine the required files, and then let ChatGPT output the code separately for each file. This can reduce the risk of contextual logic deviation. • Small Entry Point The entry point for the project should be as small as possible, small enough that ChatGPT can output a complete and useful initial version. If there are issues with the initial version, the limited code and feature scope make it easier for either ChatGPT or humans to correct. • Minimal Modules Isolate the code that needs to be modified or added, and only provide ChatGPT with the smallest possible chunk of code each time. This minimizes the chances of logical deviations. • Code Confirmation Before adding new features or logic, start a new conversation window and re-enter the project requirements and relevant code to ChatGPT for confirmation. Once confirmed, proceed with new feature development. • Specify Code If ChatGPT produces code with inconsistencies due to contextual memory limitations, re-submit the relevant code and have ChatGPT make modifications based on the most recent code. • Deep Guidance When multiple conversations fail to resolve issues, delve into the specific functional requirements and code. Provide necessary guidance to ChatGPT based on your own experience. • Self-check Issues If the problem persists after multiple conversations, guide ChatGPT to add console logs for debugging in the code. Provide the program's runtime results or error messages directly to ChatGPT for checking and correction. • Timely Rollback If the problem persists after multiple conversations, consider rolling back to a previously stable version of the conversation and allow ChatGPT to redevelop. During redevelopment, offer necessary guidance to ChatGPT based on issues encountered during previous debugging sessions or your own experience.

Utilizing machine learning algorithms for code review and optimization enhances the efficiency of the software development process, identifying areas for improvement and automating repetitive tasks.