AI Is Changing the Journey of Becoming a Software Engineer

ChatGPT Use cases and examples
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When I started learning software development, the path seemed straightforward.

Learn a programming language.

Build projects.

Read documentation.

Search Stack Overflow when you’re stuck.

Repeat.

The journey was largely about finding answers.

Today, I think that journey is changing.

Not because AI is replacing software engineers—but because it’s changing how we become one.

Earlier, progress depended on finding information

A few years ago, learning something new often meant jumping between multiple resources.

A YouTube video for the basics.

Official documentation for syntax.

A blog post for implementation.

Stack Overflow for errors.

GitHub issues for edge cases.

Learning was fragmented.

Sometimes I spent more time searching than actually understanding.

AI removed the friction of searching

Today, I can ask one question and immediately continue learning.

Instead of spending an hour looking for the right resource, I can spend that hour understanding the concept.

That sounds like a small improvement.

I don’t think it is.

When you remove friction, people ask more questions.

And asking more questions accelerates learning.

The bottleneck is no longer access to information

For years, one of the biggest challenges was finding the right information.

Today, information is available almost instantly.

That means the challenge has shifted.

The new bottleneck isn’t access.

It’s understanding.

Anyone can generate code.

Not everyone can explain:

  • Why this design is better.
  • What trade-offs it introduces.
  • How it behaves in production.
  • When it should not be used.

Those are the skills that differentiate engineers.

The value of junior engineers is changing

This is something I’ve been thinking about recently.

In the past, junior developers often spent a lot of time writing boilerplate code and looking up syntax.

AI can now handle much of that work.

So where does that leave junior engineers?

I don’t think the answer is “AI replaces them.”

I think it changes what they should focus on.

Instead of measuring progress by:

  • Lines of code written
  • Number of tutorials completed
  • Number of frameworks learned

It becomes more valuable to understand:

  • System design
  • Business requirements
  • Debugging strategies
  • Communication
  • Code reviews
  • Architectural thinking

The bar is shifting.

Learning is becoming less linear

Previously, the journey often looked like this:

Programming Language → Framework → Database → Cloud → Architecture

Now it feels more interconnected.

You can build a simple application on day one with AI’s help.

But that quickly exposes new questions:

  • Why is authentication designed this way?
  • Why do we need caching?
  • Why do APIs fail?
  • Why do distributed systems behave differently?

AI lets beginners explore advanced topics much earlier than before.

That changes the learning curve.

The skills that matter are evolving

If AI keeps getting better at implementation, I think the skills that become more valuable are:

  • Curiosity
  • Critical thinking
  • Problem decomposition
  • Communication
  • Decision-making
  • Understanding trade-offs

Those skills were always important.

AI simply makes them more visible.

My perspective

I don’t think AI makes becoming a software engineer easier.

I think it makes the journey different.

Less time is spent searching for information.

More time can be spent understanding systems, making decisions, and solving real problems.

For me, that’s the most exciting change.

The destination hasn’t changed.

But the path certainly has.

I’m curious how others see it.

If you were starting your software engineering journey today, what would you focus on differently because of AI?

2

AI-Powered Debugging: How ChatGPT Can Help Developers Solve Errors Faster

Introduction

Debugging is one of the most challenging—and often time-consuming—parts of software development. Whether you’re building a web application, integrating APIs, or working with cloud services, encountering errors is inevitable. While traditional resources like documentation, Stack Overflow, and developer forums remain valuable, AI-powered assistants such as ChatGPT are changing the way developers approach debugging.

Instead of spending hours searching through multiple resources, developers can describe an issue, provide the relevant context, and receive explanations, troubleshooting steps, and potential solutions within seconds. While AI doesn’t replace debugging skills or developer expertise, it can significantly reduce the time needed to identify and understand problems.

In this article, we’ll explore how ChatGPT can support developers during the debugging process and discuss best practices for using AI effectively.

Why Debugging Can Be Time-Consuming

Modern applications often involve multiple technologies working together. A single issue could originate from:

  • Frontend frameworks (Angular, React, Vue)
  • Backend services
  • REST APIs
  • Databases
  • Authentication systems
  • Cloud infrastructure
  • Third-party integrations

An error message rarely tells the whole story. Developers must identify the root cause, understand how different components interact, and implement a solution without introducing new issues.

This investigative process can be both complex and time-consuming.

How AI Changes the Debugging Experience

AI assists developers by acting as an intelligent troubleshooting partner. Instead of simply searching for keywords, you can describe your problem in natural language, include code snippets or error messages, and receive responses tailored to your situation.

For example, rather than searching:

“Angular NullInjectorError”

You can ask:

“I’m working on an Angular application using standalone components. I’m getting a NullInjectorError after migrating to Angular 20. Here’s my service and component code. Explain the root cause, identify what’s missing, and recommend the best solution.”

This additional context allows AI to provide a more focused and actionable response.

Understanding Error Messages

Many error messages can seem cryptic, especially when you’re working with an unfamiliar framework.

Instead of simply explaining what the error says, ChatGPT can help by:

  • Explaining the meaning of the error
  • Breaking down technical terminology
  • Identifying the likely cause
  • Suggesting where to investigate first
  • Providing example fixes

This is particularly useful for developers who are learning new technologies or working in unfamiliar codebases.

Identifying Root Causes Faster

One of AI’s biggest strengths is helping developers think beyond the immediate error message.

For example, a failed API request might actually be caused by:

  • Incorrect request headers
  • Authentication failures
  • CORS restrictions
  • Invalid payloads
  • Network configuration
  • Backend validation rules

Rather than focusing on just one possibility, AI can suggest multiple areas to investigate, helping developers narrow down the root cause more efficiently.

Learning While Debugging

Debugging isn’t just about fixing the current issue—it’s also an opportunity to deepen your understanding of the technology.

Instead of asking:

“How do I fix this?”

Try asking:

“Why is this error occurring? What concept am I misunderstanding? How can I avoid this issue in future projects?”

This transforms debugging into a learning experience and helps build long-term problem-solving skills.

Improving Code Reviews

AI can also support debugging before problems occur.

Developers can ask ChatGPT to review code for:

  • Logic errors
  • Performance bottlenecks
  • Potential null reference issues
  • Security concerns
  • Code readability
  • Best practices

For example:

“Review this TypeScript service and identify potential issues related to error handling, asynchronous operations, and maintainability.”

A proactive review can uncover potential bugs before they reach production.

Practical Debugging Scenarios

Here are some common situations where AI can assist.

Framework Errors

Understanding dependency injection issues, lifecycle problems, routing errors, or state management bugs.

API Integration

Troubleshooting authentication failures, unexpected response codes, malformed requests, or serialization issues.

Database Issues

Understanding SQL errors, query optimisation opportunities, or data consistency problems.

Build and Deployment Problems

Helping interpret build failures, dependency conflicts, configuration errors, and deployment logs.

Performance Optimisation

Identifying unnecessary re-renders, memory leaks, inefficient algorithms, or slow API calls.

Tips for Getting Better AI Responses

The quality of the response depends on the quality of the information you provide.

When asking for debugging help, include:

  • The programming language
  • Framework and version
  • Complete error message
  • Relevant code snippets
  • Expected behaviour
  • Actual behaviour
  • Recent code changes
  • Steps to reproduce the issue

The more context you provide, the more accurate and relevant the response is likely to be.

Best Practices When Using AI for Debugging

AI is a powerful assistant, but it shouldn’t replace critical thinking or established development practices.

Keep these guidelines in mind:

  • Read and understand AI-generated suggestions before applying them.
  • Test every proposed solution in a safe environment.
  • Verify framework-specific recommendations against official documentation.
  • Avoid sharing sensitive information such as API keys, passwords, or proprietary code.
  • Use AI to understand the problem—not just to copy and paste fixes.

Combining AI assistance with solid debugging techniques leads to better outcomes and stronger technical skills.

Limitations of AI

Although AI can accelerate debugging, it’s important to recognise its limitations.

AI may:

  • Misinterpret incomplete information.
  • Suggest solutions that aren’t compatible with your project.
  • Miss business-specific requirements.
  • Recommend outdated approaches if the prompt lacks version details.

Ultimately, developers remain responsible for validating and testing every solution before deploying it.

Conclusion

Debugging is an essential skill for every software developer, and AI is making that process faster, more efficient, and more educational. By explaining errors, identifying possible root causes, reviewing code, and suggesting troubleshooting strategies, ChatGPT can help developers spend less time searching for answers and more time building reliable software.

However, AI works best as a collaborator rather than a replacement for developer expertise. Thoughtful prompts, sufficient context, and careful validation are key to getting the most value from AI-assisted debugging.

As AI continues to evolve, developers who learn to combine traditional debugging techniques with intelligent AI tools will be better equipped to solve problems quickly, learn continuously, and deliver higher-quality software.

3

Prompt Engineering for Developers: Writing Better Prompts for Better Code

Introduction

As software developers, we’re used to communicating clearly with people—whether it’s through code, documentation, or discussions with teammates. With the rise of AI tools like ChatGPT, another important skill has emerged: prompt engineering.

Many developers expect AI to produce perfect answers from a one-line question. However, the quality of the response often depends on the quality of the prompt. A well-structured prompt provides context, defines expectations, and guides the AI toward a more useful solution.

After using AI regularly to learn new technologies, understand complex concepts, and troubleshoot development issues, I’ve found that spending a few extra moments writing a better prompt often saves much more time during implementation.

In this article, I’ll share practical techniques that have helped me get more accurate and actionable responses from AI.

What is Prompt Engineering?

Prompt engineering is the practice of designing clear and detailed instructions that help an AI model understand exactly what you’re trying to achieve.

Think of it like writing a requirement for another developer.

Instead of saying:

“Build this feature.”

You would usually explain:

The requirements

The technology stack

Expected behaviour

Constraints

Edge cases

The same principle applies when working with AI.

Why It Matters for Developers

AI can assist with:

Learning new frameworks

Debugging errors

Writing documentation

Refactoring code

Creating unit tests

Explaining unfamiliar codebases

Generating API integration examples

The more context you provide, the more relevant the output becomes.

Poor Prompt vs Better Prompt

:cross_mark: Poor Prompt

Explain Angular.

This is too broad.

:white_check_mark: Better Prompt

Explain Angular Signals with practical examples. Compare them with RxJS, discuss their advantages and limitations, and provide a simple component example using Angular 20.

Notice how the second prompt clearly defines:

Topic

Comparison

Version

Expected output

Example requirement

Include Context

One of the biggest mistakes developers make is assuming the AI already understands their project.

Instead of writing:

Fix this error.

Try:

I’m working on an Angular 20 application using standalone components and RxJS. I’m receiving the following error during compilation. Explain the root cause, suggest the best fix, and mention any potential side effects.

Providing project context usually leads to much more relevant responses.

Specify the Desired Output

Different tasks require different kinds of answers.

Rather than saying:

Create authentication.

Be specific:

Design a JWT authentication flow for a REST API using Node.js. Explain each step, include sample request/response payloads, and describe common security best practices.

This helps AI tailor its response to your needs.

Break Down Complex Problems

Instead of asking for an entire application in one prompt, divide the task into smaller parts.

For example:

Design the architecture.

Explain the folder structure.

Generate the API layer.

Create reusable UI components.

Add error handling.

Write unit tests.

Breaking problems into manageable steps often produces higher-quality results.

Ask AI to Explain Its Reasoning

Sometimes understanding why a solution works is more valuable than the solution itself.

Instead of requesting only code:

Generate the code.

Ask:

Generate the solution, explain why each approach was chosen, discuss alternative implementations, and highlight any performance considerations.

This approach is particularly useful when learning a new technology.

Iterate and Refine

Prompt engineering is rarely a one-step process.

If the initial response isn’t exactly what you need, refine the prompt.

For example:

First prompt:

Explain dependency injection.

Follow-up prompt:

Can you explain it using a real-world Angular application and compare it with manual object creation?

Each refinement narrows the scope and improves the quality of the response.

Practical Prompt Examples

Learning a New Framework

I’m new to NestJS but have experience with Angular. Explain the core concepts, compare them with Angular where appropriate, and provide a beginner-friendly learning roadmap.

Debugging

I’m receiving a “NullInjectorError” in Angular 20. Explain the possible causes, how to identify the root cause, and provide recommended fixes with code examples.

Code Review

Review the following TypeScript code for readability, performance, maintainability, and Angular best practices. Suggest improvements without changing the functionality.

API Integration

Generate an Angular service that consumes a REST API using HttpClient. Include proper error handling, loading states, and explain each step.

Best Practices for Prompt Engineering

Clearly define your objective.

Provide relevant project context.

Mention the technologies and versions you’re using.

Specify the format of the response you expect.

Break large tasks into smaller ones.

Ask for explanations instead of only solutions.

Refine your prompts based on previous responses.

Always validate AI-generated code before using it in production.

Final Thoughts

Prompt engineering isn’t about learning a new programming language—it’s about improving how we communicate with AI. As developers, we’re already accustomed to writing clear requirements and breaking down complex problems. Applying the same mindset when interacting with AI can significantly improve the quality of the responses we receive.

AI should be viewed as a development assistant rather than a replacement for engineering expertise. It can accelerate learning, reduce repetitive work, and offer new perspectives, but the responsibility for validating, testing, and maintaining the final solution remains with us.

The better the prompt, the better the collaboration between developer and AI.