Business

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.

 
 

Top Process Automation Tools For Businesses in 2023

Process automation is a big topic on everyone’s mind in the face of AI. What jobs will be overtaken by computers? How can companies cut costs by leveraging business process automation tools (BPA)? How can I run with technology instead of away from it?

There are a lot of questions and speculation, but the ultimate question is; are companies putting their money where their mouth is when it comes to integrating AI? Well, research is showing us that of about 2600 companies surveyed globally, more than 94% believe AI is critical to success, almost 80% have begun implementing a variety of AI solutions, and 82% found a boost in job satisfaction from AI tools. 

If you’re curious about the direction of AI's future with software, watch this video.

Integrating AI is something companies must do, but how they execute this integration will vary. Of the use cases for process automation, the biggest one we’re seeing is end-to-end visibility, which essentially is what allows companies to track their entire workflow from start to finish. Why would they want to do that? It’s quite simple: by having end-to-end visibility, companies can identify bottlenecks and proactively address them.

Among many reasons for incorporating process automation, efficiency and cost-cutting are two of the most important factors. Let’s use the example of a plastics manufacturing company that uses inventory management, production scheduling, and quality control software as its main time-consuming, repetitive tasks. BPA in this case would automate all of these tasks, and now the staff’s role shifts its focus to oversight, creativity, and decision-making. For most companies today, that is the goal; streamline and optimize operations end-to-end.

In 2023, there are several tools that are most commonly used across industries to make this happen. Here are some to pay attention to:

1) UiPath

UiPath is a robotic process automation (RPA) platform that takes over repetitive tasks (as most of these tools do) at scale. It has a visual drag-and-drop interface for designing automation workflows and integrates with various applications and systems.

For instance, a human resources department can use UiPath to automate the employee onboarding process, where the software automatically generates employee contracts or updates employee records in HR systems, and notifies the relevant stakeholders, reducing manual effort. Now you have a system that can be scaled. 

2) Pega

Pega is a platform that combines business process management (BPM) and intelligent automation. Pega is a comprehensive platform that offers a unified view of the entire business process and ideally leads companies to a solution for end-to-end automation. For example, a retail organization can use Pega to automate its order management process. The platform can allocate resources, track inventory levels, and then adjust production schedules based on current demand as well as forecasted demand. 

3) Blue Prism

Blue Prism's RPA software can automate rule-based tasks (Data entry, processing invoices, QC, etc) across different departments. For instance, think of a healthcare organization, that’ll use Blue Prism to automate something like claims processing, where the software first validates claims, then checks for errors, and initiates payment processes.

Blue Prism is best utilized for repetitive tasks that ideally can be scaled. For instance, we used examples from healthcare, but email marketing is another common task for companies that would benefit from scalable RPA. 

4) Appian

This is a low-code development platform that’s meant for companies to design and automate workflows. Appian connects with data sources and external applications, supporting standard protocols and APIs like REST, SOAP, and JDBC, which makes integration easy. This is what’s going to attract something like a big manufacturing company that would use this to speed up their approval process or integrate it with systems for inventory management.

5) Automation Anywhere

Automation Anywhere is one of the top RPA platforms that are great for enterprise automation. This is another platform with a drag-and-drop interface (easy to use) and end-to-end process automation. This is one that a FinTech (or other various large-scale entities) could leverage to ultimately reduce manual effort and scale the operation. 

The Value In BPA

Think about a 30-year multi-billion dollar business with tens of thousands of employees. How can they leverage process automation across the board? Ultimately, it comes down to recognizing what can be optimized and what’s in the best interest of the product or service's long-term sustainability. An easy one is Amazon— if tomorrow they decided to get rid of warehouse workers and fully leverage RPAs like automated guided vehicles (AGVs), and intelligent warehouse management systems, their inventory management would streamline.

These are the kind of gaps that companies need to be looking for in the coming years. It’s less about what you do and all about how efficiently you do it. 

The Takeaway

Finding gaps in your current processes can be difficult without a thorough analysis and understanding of your operations. This is where AI consulting comes in. By leveraging this level of expertise, companies will identify latent pains and receive the most suitable automation solutions for their specific needs. It’s not a cookie cutter; it’s a comprehensive approach tailored to the unique challenges and goals of each business.

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.