How To Use ChatGPT To Skyrocket Your Engineering Team's Productivity

Written by:
Team Middleware

Table Of Contents

The pressure to deliver high-quality software at breakneck speed is a reality every engineering team faces. Technical debt piles up, processes become bottlenecks, and communication misfires can derail even the best-planned plans. 

AI is already hyped and being used extensively, not just for generating clever tweets or code snippets, but also for genuinely transforming how software teams operate, we believe the launch of GPT-4o changes the game. 

Yet, many organizations are still underutilizing its capabilities, sticking to manual processes and legacy workflows that kill productivity.

This quick guide dives into the technical applications of ChatGPT within SDLC.

We will share a few practical ideas with you on how it can streamline collaboration, enhance code quality, and ultimately, accelerate your time-to-market.

Understanding ChatGPT's Arsenal

ChatGPT has evolved far beyond a simple text generator. It's an interesting and impressive tool capable of:

  • Code Generation: Generate snippets, templates, and even entire functions based on specific requirements.
  • Debugging and Error Resolution: Analyze errors, suggest fixes, and provide explanations for complex issues.
  • Documentation: Create comprehensive documentation from code comments and specifications.
  • Routine Task Automation: Streamline repetitive tasks like setting up CI/CD pipelines, managing infrastructure, or generating test cases.
  • Natural Language Processing (NLP): Go beyond code – draft emails, create reports, and even facilitate team communication.

But It's Not Perfect...

ChatGPT has its limitations:

  • Contextual Understanding: May struggle to grasp nuanced or complex instructions.
  • Input Quality Dependency: The better your prompts, the better its output.
  • Bias and Accuracy: It's trained on vast data, but biases can exist, and accuracy is not always guaranteed.

Unlocking Competitiveness

Ignoring the potential of AI like ChatGPT puts your team at a disadvantage. By leveraging such AI tools in your software delivery pipeline you can:

  • Accelerate Development: ChatGPT helps expedite code creation, review, and debugging, freeing up time for more complex and creative tasks.
  • Enhance Collaboration: Automated report generation and meeting summaries keep the team aligned.
  • Facilitate Knowledge Sharing: Generate comprehensive documentation and provide on-demand explanations of complex concepts.

Some Prompt & Response Examples

Code Reviews

Get quick feedback on pull requests, suggestions for improvements, and potential issue flags. A great prompt generator to look at is Anthropic’s Prompt Generator. In fact, they even link to their Google colab notebook in case you want to look under the hood.

You can also leverage Middleware Open Source to understand the software delivery metrics and then optimize your process based on that data.

One really important thing to keep in mind is that you should take care while sharing business logic, secrets, keys etc with LLM models.

Example Prompt:

Analyze the following pull request for potential security vulnerabilities, performance bottlenecks, or deviations from our coding standards

[Paste the pull request code here]

Automated Testing

Generate test cases and data to ensure thoroughness and coverage.

Example Prompt:

Generate a test case for the following function that validates the "calculateInterest" function correctly handles negative interest rates:

public double calculateInterest(double principal, double rate, int time) { // ... existing implementation ...


ChatGPT Response:


public void testNegativeInterestRate() {

    double principal = 1000.0;

    double rate = -0.05;  // Negative interest rate

    int time = 5;

    double expected = 750.0;  // Expected result with negative interest

    double actual = calculateInterest(principal, rate, time);

    assertEquals(expected, actual, 0.01);  // Allow for slight rounding differences


Jira & Slack Integration

Automate ticket creation/updates and streamline communication.

Example Prompt:

Create a Jira ticket for the following issue:

High priority: User reported payment processing failure on checkout page. Error code 402.

Incident Response

Utilize ChatGPT for faster triage and resolution by creating guided playbooks.

Example Prompt:

Generate a troubleshooting guide for the following incident:

Error: "Connection to database timed out."

ChatGPT Response:

  1. Verify database connectivity: Check network connectivity and firewall settings.
  2. Check database status: Is the database server running? Monitor resource usage.
  3. Review application logs: Look for error messages related to the database connection.
  4. Restart database service (if applicable): Try restarting the database if it's unresponsive.
  5. Escalate to DBA team: If the issue persists, involve the database administrators for further investigation.

Strategies to Maximize the Impact of ChatGPT

Embrace a "Learning" Mindset

Encourage your team to explore and experiment with AI tools, integrating it into their daily workflows.

Quality Input, Quality Output

Train your developers to write clear, specific prompts for accurate results. 

Here is a great free YouTube video course on Prompt Engineering by FreeCodeCamp on their YT channel that can get you and your team started with a really strong foundation.

Here are 2 examples:

Example 1: Code Refactoring

Bad Prompt:

Make this code better.

function calculateTotal(items) {

  let total = 0;

  for (let item of items) {

    total += item.price * item.quantity;


  if (total > 1000) {

    total *= 0.9; // 10% discount


  return total;


This prompt is too vague. "Better" is subjective. ChatGPT might make stylistic changes, but won't know if there are hidden bugs or performance issues you want addressed.

Good Prompt:

Refactor the following JavaScript function to improve readability and maintainability. Additionally, identify potential optimizations to improve its performance when dealing with large arrays of items:

[Paste the same function code here]

This prompt sets clear goals: better readability, maintainability, and performance for large datasets. This gives ChatGPT specific directions to work with.

Example 2: Incident Response

Bad Prompt:

Server is down, what do I do?

This is too broad. ChatGPT doesn't know your infrastructure, what "down" means (500 errors? Network outage?), or your escalation procedures.

Good Prompt:

Our MySQL database server, 'db-primary', is unresponsive. Users are reporting errors on the checkout page. Our monitoring shows a spike in connection attempts. Here are the relevant logs: [Paste Log Snippet]

What are the most likely causes and the immediate troubleshooting steps I should take?

This provides crucial context: the technology involved, symptoms observed, and even log data if possible. ChatGPT can now offer targeted advice relevant to the situation.

Continuous Feedback

Encourage your team to tell ChatGPT if it’s doing a bad job to help improve its performance. Consistent back and forth helps with quality of output right in the first go after a while.

Integrate with Existing Tools

Leverage ChatGPT's API to seamlessly integrate it with your existing toolchain (e.g., Jira, Slack, GitHub).

Final Thoughts

ChatGPT isn't just another Silicon Valley buzzword. It's a legit force multiplier for your engineering org.

We're talking about streamlining code reviews, automating the grunt work that eats up dev time, and making knowledge sharing actually useful.  

We at Middleware help the engineering team become productive, worry not, you don't need to register to know what we do, simply get started with Middleware Open Source by checking out the Github repo here, make sure to leave a star if you like what you see.

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