Generating code for everyday tasks

One of the most tangible ways to use AI is for writing basic code. Whether it's repeatable code patterns, SQL queries, or API integrations, AI can help accelerate foundational tasks.

Example prompt: "Write an SQL query to select all customers who placed an order in the last 30 days."

With the right prompt, AI can complete these tasks in seconds, giving developers more time for complex projects. The result can serve as a starting point, which developers can then fine-tune and expand.

Exploring complex concepts and identifying connections

AI can also assist in understanding complex relationships between concepts, such as microservices architectures or system integrations. This is especially useful when designing systems where multiple services interact.

Example prompt: "Explain the pros and cons of using an event-driven architecture for an e-commerce platform."

With prompts like this, AI can quickly provide an overview of key factors, which can be useful for planning new infrastructure or preparing for technical discussions with stakeholders.

Troubleshooting production environment issues

AI can offer suggestions for addressing performance or stability issues and help uncover cause-and-effect relationships in production environments.

Example prompt: "Why has my API response time increased since the last update?"

By entering this question, AI can suggest potential causes, such as changes in dependencies, increased user load, or a misconfigured load balancer. This insight helps diagnose problems and prevent further issues.

Automating project planning and preparation

Project preparation can be time-consuming, especially when multiple teams are involved. AI can assist with creating project plans, risk analyses, and checklists specifically for IT environments.

Example prompt: "Create a project plan and risk analysis for implementing a CI/CD pipeline for an e-commerce website."

This provides a starting point for a project plan, outlining key steps and risks such as possible downtime or integration challenges. It helps you focus directly on the main project challenges.

Code review and quality improvement

AI can also act as a "pair programmer," reviewing code and suggesting improvements. By having AI analyze the code, developers can save time during code review processes.

Example prompt: "Check this Python code for inefficiencies and suggest improvements."

With this prompt, AI can highlight areas for improvement, such as reducing repetition, enhancing readability, or simplifying complex logic. This speeds up code reviews and adds an extra set of "eyes" to optimize code.

Simulating user interactions and test scenarios

For developers working on frontend or user-facing applications, AI can help simulate user interactions. This is useful for testing UI elements, input validation, or error-handling scenarios.

Example prompt: "Simulate a user logging in and navigating various pages within a web application using Selenium."

AI can generate the base code for a test script, allowing QA specialists and developers to create test scripts more quickly and view the system from the user's perspective. This increases the chances of identifying issues early on.

Utilizing data analysis and machine learning predictions

With AI, you can quickly set up data analyses or even machine learning models that provide valuable insights without requiring deep ML expertise.

Example prompt: "Build a machine learning model to predict churn for users of a SaaS platform using Python."

With this instruction, AI generates a starting point for a predictive model that can help improve customer retention. AI can also suggest feature engineering and model selection, accelerating the development process.

Ensuring security and reliability of AI output

When using AI in your projects, it’s important to pay attention to security. Ensure all data is well-protected and use two-factor authentication when working with generative AI tools, especially with sensitive information. AI can save you a lot of time, but subject matter expertise remains essential for verifying generated output.

Get more out of AI

Interested in successfully using AI for software development, system administration, or IT architecture? Contact us for a no-obligation conversation on how AI can enhance your work or your organization.