Best Chat GPT Plug-ins For Software Development

We know about the prompts Chat GPT has to offer software developers, which are great starting points— but truly, they are nowhere near the full scope of leveraging streamlined workflows from AI. Since the dawn of its existence, coding has been about 3 things; creativity, problem-solving, and innovation. With artificial intelligence, the horizon for these aspects of coding expands exponentially.

Plug-ins elevate this further, especially when a programmer knows exactly which to use and when. What plug-ins do is leverage AI algorithms to cut down the development process by automating tasks and getting data-driven insights. A typical scenario for development with plug-ins might look like this:

Scenario of Leveraging Plug-ins

A programmer is working on developing a web application for a client. The project involves developing systems for user authentication and authorization, implementing complex business logic, integrating with external APIs, and load balancing. The deadline is also tight. 

To start, they use a plug-in that automates the process of setting up secure user authentication systems, including user registration, login, password encryption, and session management. Once that’s done, they go to an AI-driven tool that will give them 3 things; intelligent suggestions, code templates, and algorithms for handling not just super complex workflows, but also calculations. Still with me? We’re almost there.

As the project involves integration with external APIs, the programmer moves to an AI-based tool that simplifies the process of connecting to external services. The main purpose of this tool is 4 things: automatically generating code for authentication, making API requests, handling responses, and managing errors. By now, communication with external APIs is seamless and they move on to the last phase. 

They need load balancing for performance and scalability. For this reason, they use a tool that does 3 things: monitors server loads, distributes incoming traffic across servers, and optimizes resource allocation. With this, the workload can be distributed which ultimately lets the system handle user traffic without compromising stability. 

Throughout this entire process, the programmer is leveraging a tool that analyzes code and performance metrics. The Chat GPT plug-ins utilize these insights and data from the analysis to generate code snippets and insights. So if you thought plug-ins were limited to providing prompts, think again. With that said, here are some Chat GPT plug-ins that developers need to pay attention to:

Zapier

This is likely the most popular integration tool as it integrates over 5,000 apps with ChatGPT. You can connect apps like Google Sheets, Gmail, and Slack directly to ChatGPT. Users can leverage any of Zapier's 50,000 actions, such as search, update, and write to automate tasks. 

Code Interpreter

Although currently in a closed alpha phase, this plug-in is very promising for the future of software development. It enables the execution of Python code directly within a chat session with ChatGPT. The plugin allows users to upload and download files, make code adjustments, and even receive suggestions and modifications from ChatGPT. 

ChatWithGit

ChatWithGit is a plugin that enables users to search GitHub directly within the ChatGPT interface. It allows users to find relevant code snippets and provides a preview of the code along with a link to the corresponding repository. This feature saves developers time by leveraging existing code resources on GitHub without having to reinvent the wheel.

Visual ChatGPT Studio

Visual ChatGPT Studio is an extension from the Visual Studio Marketplace. It integrates with Visual Studio. Its features include method code autocompletion, adding unit tests, bug detection, method optimization, explanation writing, commenting, providing summaries for C# methods, and users can ask questions (code-related questions) and get answers within the editor.

Prompt Perfect

The Prompt Perfect plug-in helps users create effective prompts by optimizing and rewording them for better results. It’s also not exclusive to ChatGPT. All you do as a user is begin your prompt with “perfect” followed by your request. 

Wolfram Alpha

The Wolfram Alpha plugin addresses ChatGPT's limitations in math. It gives ChatGPT access to computation, math functionalities, curated knowledge, and real-time data through Wolfram Alpha and the Wolfram language. Using this plugin, ChatGPT can give you improved responses and visualizations for math-related queries.

Link Reader

Link Reader enables ChatGPT to extract and analyze information from various types of links, like PDFs, PPTs, Word docs, and all the fun stuff alike. Users then have ChatGPT translate, summarize, interpret, and even analyze the contents of the given link, to get insights far more in-depth than a typical search engine.

Moving Forward

Emad Mostaque's statement that "There are no developers in 5 years" is something companies in the tech space need to really think about. The amount that one person can achieve increases exponentially with AI-powered tools. And trust me, you won’t lose your job to a machine, but the person who knows how to leverage machines will blindside you.

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

33 Chat GPT Prompts For Software Developers

There’s the notion that some people know how to use Chat GPT far better than others simply by the prompts they use. It’s not an unfair statement by any means, but it required some digging on our part to uncover the best Chat GPT prompts tailored specifically for software developers. As developers, we are always on the lookout for tools and techniques that can enhance our productivity and make our lives easier.

Now, what we outline here is by no means definitive and should instead be taken as signifiers of the endless possibilities that Chat GPT offers to programmers. With that said, here are some prompts for you to try:

Basic Prompts

• “Provide an example of code that achieves [specific task]”

• “Share the best practices for [specific language or framework] development”

• “Assist me in debugging this code snippet. I'm facing an issue with [specific problem]”

• “Suggest efficient algorithms for solving [specific problem]”

• “Offer recommendations to improve the user experience of my application”

• “Provide insights on the latest trends in [specific technology]”

• “Explain the concept of [specific concept] in simple terms”

The blank spaces within these prompts are up to you. If you wanted to get more specific, it’d look something like this:

More Specific Prompts

“Create a Python script to parse JSON files and extract specific data with the following requirements:

• Ability to handle large JSON files efficiently

• Support for nested JSON structures

• Robust error handling and logging mechanism

• Develop a Node.js microservice for e-commerce that includes endpoints for user authentication, product listing, cart management, and order processing, and adheres to the RESTful design pattern”

• “Write a Java function to filter an ArrayList based on a given condition with the following inputs: the ArrayList of objects, the filtering condition as a lambda expression, and the expected output: the filtered ArrayList”

• “Design a C++ algorithm to solve the travelling salesman problem using the branch and bound strategy”

• “Implement a JavaScript function that handles file uploads asynchronously with the following inputs: the file object, the target directory, and the expected output: a success or error message indicating the status of the upload process”

• “Provide a code snippet in Python that calculates the average of a list of numbers”

• “Design a RESTful API using Node.js and Express for a music streaming service. Include endpoints for user registration, playlist creation, song recommendation, and user authentication”

• “Write a C# function that checks if a given string is a palindrome and returns a boolean value”

• “Develop a Java program that implements a binary search algorithm for finding an element in a sorted array”

• “Create a PHP script that generates a random password with specific requirements such as a minimum length, inclusion of uppercase letters, lowercase letters, numbers, and special characters”

• “Design an object-oriented class structure in C++ for a library management system. Include classes for books, patrons, and the ability to handle book borrowing and returning”

• “Implement a JavaScript function that converts a string representation of a date into a Date object and performs date manipulation operations such as adding or subtracting days”

• “Develop a Python script that interacts with a database to retrieve specific data based on user input. Ensure the script handles database connections, executes queries, and provides appropriate error handling”

• “Design a web application using Django framework for an online marketplace. Include features such as user registration, product listing, shopping cart management, and order processing”

• “Write a Ruby method that sorts an array of objects based on a specific attribute in ascending order. Consider inputs such as the array of objects and the attribute to sort by”

• “Implement a TypeScript function that validates user input in a form and provides real-time feedback. The function should handle input validation rules, display error messages, and trigger validation on input change”

• “Create a PHP script that generates QR codes for a given set of data. Ensure the script handles data encoding, QR code generation, and provides the generated QR code as an output”

• “Design an algorithm in Java to find the shortest path between two nodes in a graph using Dijkstra's algorithm. Consider inputs such as the graph structure, start and end nodes, and expected output of the shortest path”

• “Develop a Node.js microservice for a chat application that includes features such as user authentication, real-time messaging, and message history retrieval”

• “Write a Python function that calculates the factorial of a given number recursively. The function should handle non-negative integers as inputs and provide the calculated factorial as an output”

• “Implement a C# program that reads and processes data from a CSV file. The program should handle parsing the CSV file, extracting specific columns or rows, and performing data manipulation or analysis”

• “Create a JavaScript function that generates a random colour code in RGB format. The function should provide the generated colour code as an output”

• “Develop a Python script that scrapes data from a website and generates a report summarizing specific information, such as product prices, ratings, and reviews”

• “Create a mobile app using React Native that allows users to create and share personalized digital greeting cards with customizable templates, text, and images”

• “Write a Java program that simulates a simple banking system with features like account creation, deposits, withdrawals, and balance inquiries. Implement error handling for cases such as insufficient funds or invalid transactions”

• “Design a web-based project management tool using Ruby on Rails, which includes features like task assignment, progress tracking, file sharing, and team collaboration”

• “Implement a sentiment analysis algorithm in Python that analyzes a text document or a stream of tweets and determines the overall sentiment (positive, negative, or neutral) of the content”

These prompts are going to be something you experiment with and tailor to your needs as you go through the programming process. You can get super specific and continue to give the output back to Chat GPT until you get the ideal response. For instance, if you wanted to use the first more specific prompt we listed; to develop a Python script that will parse JSON files and extract specific data, you can start with the prompt:

“Create a Python script to parse JSON files and extract specific data with the following requirements…”

It’s not likely that the system is always going to give you exactly what you’re looking for on the first request, in that case, say something like “I've reviewed the initial code generated, but I'm still encountering issues when parsing nested JSON structures. How can I modify the code to fix this?”

The key is to be as descriptive as possible, if you just say “It’s not working”, you’re going to be going back and forth with the system for a long and painful time. Treat Chat GPT like your programming therapist; “The code fails to retrieve data from nested objects. I receive a 'KeyError' when trying to access certain fields. How can I modify the code to fix this?".

What’s next?

Chat GPT just announced Code Interpreter, which is going to revamp how we understand the coding process beyond using prompts in Chat GPT. However, knowing how to use prompts like these will certainly be a valuable prerequisite when moving on to more advanced systems like Code Interpreter.

If you’re more interested in integrating AI capabilities into your company, check out our AI consulting page for more information.

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

How Your Business Benefits From Cloud Computing and AI/ML Synergy

Cloud computing has become central to scaling your business in 2023. What we’re uncovering is that AI and ML capabilities in the cloud make businesses more efficient, strategic, and insight-driven. Of course, while leveraging new technologies is highly subjective in terms of use cases, understanding when an opportunity arises to get the most out of your workflows from the least amount of effort will directly correlate to longevity. 

Cloud computing, Artificial Intelligence, and Machine Learning are tools that were created for this very reason which makes the synergistic integration of the three quite lucrative for organizations. 

Now when it comes to placing AI in a cloud environment, it’s there to enhance the operation, not replace it. Saas companies are incorporating AI and ML into their bigger software packages which is an effort focused on end-user functionality and the UX overall. 

Two Industry Examples

1) Insurance:

Practices in the insurance industry are limitless when it comes to integrated extensions from AI and ML in the cloud. Traditionally as an insurance broker, you have to manually assess customer information, research policies, and make recommendations. With process automation enabled by AI and ML in the cloud, you expedite that entire process. Here are some examples:

Claims Processing and Fraud Detection:

• Cloud-based AI and ML tools can analyze claims data and assess validity.

• ML algorithms can identify patterns of fraudulent claims by analyzing past data, trends in customer behaviour, and other various external factors, which ultimately improves fraud detection and prevention.

Risk Assessment and Underwriting:

• AI-powered algorithms in the cloud can analyze customer data, market trends, and historical claims to accurately assess risk.

• ML models can generate underwriting recommendations, ensuring that policies align with customer needs and risk profiles.

2) Manufacturing:

In the manufacturing industry, the integration of cloud computing, AI, and ML changes the entire production process as well as overall quality control. Oversight and automation are enhanced through the following:

Production Process Optimization:

• Cloud-based AI and ML platforms enable real-time monitoring of production lines, capturing data from sensors and IoT devices.

• AI algorithms can analyze this data to identify bottlenecks and areas for process improvement.

• ML models integrated with the cloud can predict equipment failures and in turn, optimize maintenance schedules and minimize downtime.

Quality Control and Detecting Defects:

• Cloud computing allows for the storage and processing of vast amounts of quality control data, be it images, sensor readings, or even product specifications.

• AI algorithms in the cloud can automatically analyze this data and recognize defects, deviations from standards, and of course anomalies.

• ML models can continuously learn from historical data to improve defect detection accuracy and ultimately enable proactive quality control measures.

Supply Chain Management:

• Cloud-based AI and ML solutions provide visibility into the supply chain, integrating data from various sources such as suppliers, logistics partners, and inventory systems.

• AI algorithms can optimize inventory levels and demand forecasting, which aids in logistics planning to minimize stockouts, reduce costs, and improve delivery times.

• ML models integrated with the cloud can identify patterns in demand, supplier performance, and market trends, which allows for more accurate procurement decisions.

Safety and Predictive Maintenance:

• Cloud-based AI systems can analyze data from IoT sensors and machinery to monitor and assess safety conditions. 

• AI algorithms can identify potential safety hazards and issue alerts to prevent accidents.

• ML models in the cloud can predict equipment failures based on historical data and sensor readings, which makes maintenance proactive and minimizes downtime in addition to optimizing asset performance.

Technical Logistics

From a technical standpoint, integrating AI and ML in cloud computing involves leveraging various specific tools, languages, and frameworks that can be quite complex. To give you a sense of what that’d look like, here is a rough breakdown of the technical aspects:

• Cloud Computing Infrastructure:

For cloud computing, you’ve got your pick at service providers which include Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). These platforms provide services such as virtual machines (EC2 instances), storage (S3, Azure Blob Storage), and of course, data processing (AWS Lambda, Azure Functions) which leads to the next point.

• Data Storage and Processing:

To handle large volumes of data, IT teams can utilize cloud-based storage solutions like AWS S3, Azure Blob Storage, or Google Cloud Storage. For data processing, distributed processing frameworks such as Apache Hadoop and Apache Spark are a couple of options. 

• AI and ML Libraries/Frameworks:

Python is the most widely used language in AI and ML, because of its extensive libraries and frameworks. Some great libraries for AI and ML in this case include:

• TensorFlow: An open-source framework developed by Google for building ML models, particularly neural networks. It provides APIs for high-level model development and deployment.

• PyTorch: Another popular open-source ML library with dynamic computational graphs, making it well-suited for research purposes.

• Scikit-learn: A versatile library that provides a range of algorithms and tools for data preprocessing, feature selection, and model evaluation.

• Keras: A user-friendly deep learning library that runs on top of TensorFlow, simplifying the development in addition to training deep neural networks.

Developing and Deploying The AI Model

The team can use TensorFlow or PyTorch to build the actual AI model. The reason we isolated those two is that they offer APIs for creating, training, and most importantly evaluating the model. They can then be deployed using cloud-based services like SageMaker, Azure Machine Learning, or Google Cloud AI Platform, which all have managed environments for training and deploying ML models at scale.

Integrating The Model

To ensure effective integration and interoperability between cloud services and your AI/ML models, API frameworks like REST (Representational State Transfer) or GraphQL can be some good options. These frameworks are what allow for communication and data exchange between different components of the system.

Moving Forward

Once you’ve integrated everything, use tools like CloudWatch or Azure Monitor to gain some insight as to how your system is utilizing its resources and always be ready to make adjustments.

The Takeaway

Innovation on top of what many already consider cutting-edge technology is a recipe for success. Cloud computing, artificial intelligence, and machine learning are all powerful tools that, when integrated synergistically, can revolutionize businesses across various industries. The key takeaway from this discussion is that the combination of the three offers tremendous potential when it comes to driving strategic decision-making and operating as efficiently as possible. 

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.