DEV Community: Andrew Dabych

Should a Tech Lead Prepare AI Instructions for the Team?

Andrew Dabych — Tue, 15 Apr 2025 10:07:25 +0000

AI is no longer just a tool. It's an assistant that can be integrated into daily development. But like any newcomer to a team, it's only effective when given clear instructions.

Why is this important for a tech lead?

AI as a new team member

Imagine: a newcomer joins your team. They're fast and can work with a large amount of context, but they don't know your architectural approaches, where things are located in the project, or what "good code" looks like. Without instructions, they'll start acting chaotically.

Quality of instructions = quality of results

AI doesn't guess intentions. What you tell it is what you'll get. Instructions need to be clear and structured. Simple examples:

❌ "Process the order"
✅ "Write a Go function that processes a JSON order, saves it to Postgres, and logs it to Redis."
❌ "Check my code"
✅ "Analyze this JavaScript code for potential memory leaks and security vulnerabilities, especially in user data processing functions."
❌ "Do refactoring"
✅ "Refactor this React component by splitting it into smaller sub-components following the single responsibility principle. Apply hooks instead of class components and use TypeScript for props typing."

Teams are changing how they work

Developers are shifting from "writing code" to "managing the code creation process." And AI is a great tool if used correctly.

What should be in AI instructions?

Prompt templates: Create and store ready-made queries in Markdown files. This allows you to quickly apply proven templates with minimal changes for different tasks — writing tests, refactoring, fixing bugs.
Defining AI responsibility boundaries: Clearly define which tasks can use AI and which cannot. Critical code, especially related to security and financial operations, should always be verified by humans.
Workflow integration: Document the prompts used in Pull Request descriptions. This helps colleagues understand how the code was generated and use similar approaches in the future. Store successful prompts as part of project documentation.
Team training: Organize regular sessions where developers can share effective AI practices and demonstrate which approaches work best for your project.

Examples of prompts that not every AI agent will understand

Write a Go function that transforms regular numbers into success spells for your sprint. No external libraries, but with an extra portion of humor.

Rewrite this function so that even a sleepy junior at 6 AM can understand what's happening here. And let the code shine like a star on a Christmas tree!

Create unit tests that check this function with such thoroughness as if it controls a space rocket launch. Use testify, but add funny comments about each test case.

Development cycle with AI

AI doesn't just help write code. It changes the entire approach to development. Here's what a modern development cycle might look like when AI is part of daily work.

Explanation of stages:

Task Definition — clear requirement specification for AI. Detailed technical descriptions of the task yield better results.
AI: Prompt Creation — developer creates structured instructions that precisely communicate the code requirements.
AI: Code Generation — process where AI creates code based on the prompt that should meet the specified requirements.
Manual Code Review — important step of checking the generated code for compliance with project standards and requirements.
Testing — verifying code functionality through automated and manual tests. Thorough testing helps identify hidden errors.
AI: Refactoring or Optimization — improving the initial code version with more specific requirements and context in mind.
Code Review with prompt description — team discussion not just of the code, but also the approach to formulating the task for AI.
Merge and deploy — final stage where the verified code is integrated into the project and deployed in the production environment.

What will the team gain?

Development acceleration (up to +30% on typical tasks and +200% on coming up with excuses for the manager)
Fewer errors in simple parts of code (and more creative errors in complex ones)
More focus on creative solutions (and on scrolling Reddit while AI works)

AI in Software Development: Tools and the Future of the Profession

Andrew Dabych — Wed, 26 Mar 2025 09:51:56 +0000

AI in Software Development: How AI is Changing the Developer Profession

The world of software development is changing so fast that you can't even update your favorite code editor in time! AI is no longer just a helper — it takes over the routine, tests, optimizes, and even suggests architectural solutions. Should we be afraid? No, but it's important to improve our skills in interacting with AI, as this wave is definitely not slowing down.

AI Tools That Are Already Changing the Game

AI is no longer just "autocompletion" — it's a full-fledged partner in development. Here are the top tools that are currently shaking up the industry:

GitHub Copilot – built into VS Code, it helps write functions, finds optimal solutions, and fixes bugs. Instead of googling, just ask Copilot.
Cursor – this AI editor lets you write code as if you’re simply talking to a partner. It integrates GPT-4 Turbo and really speeds up your work.
Replit AI – for those who want to quickly create prototypes without much hassle. It's suitable for both beginners and experienced developers.
Windsurf – an AI for team collaboration, understanding context, and helping work with large codebases.
Zed – a breath of fresh air in the world of editors! The main focus here is AI and fast collaboration with other developers. This is an editor where code is written almost at the speed of thought. Its AI features make it one of the most promising tools for teamwork. By the summer of 2025, the Agentic Mode, already available in other popular AI editors and plugins, is expected. Check out their development roadmap here.

Vibe Coding – Coding with Words, Not Hands

Vibe-coding is the next-gen trend, where you simply explain to AI what you need, and it writes the code for you. This is no longer science fiction, it’s reality. The better your request, the better AI delivers. However, even with this, to ensure the result is truly high-quality, you need to understand what you’re asking for. AI will help, but to make sure the outcome meets your standards, you have to be experienced and capable of evaluating the final product.

How the Role of the Developer is Changing

Thanks to AI, you no longer need to spend hours on routine tasks. What matters now:

Designing architecture properly
Understanding how services interact
Working on performance
Ensuring code quality and security

The developer of the future is not just someone who knows syntax. They are a strategist who knows how to work with AI and make everything faster, more efficiently, and smarter.

What Lies Ahead?

The future of development will be even more interactive, and the role of the developer will change. Instead of writing code manually, you will focus more on managing processes, coordinating AI assistants, and creating complex systems in collaboration with machines.

Career prospects will also change. The demand will be for not just coders but solution architects, AI prompt engineers, and experts in AI integration into business processes. Those who adapt faster will get cool opportunities and career boosts.

One thing is for sure: AI in development is not a hype, it's a new reality. Those who master these tools first will become industry leaders. So now is the time to take action and try it out!

Keep Learning, Stay Successful in Tech Industry

Andrew Dabych — Tue, 04 Mar 2025 10:21:11 +0000

Growth as the Key to Success in Tech

In Tech, you can’t just learn something once and work indefinitely as a top specialist with the same knowledge. Technologies evolve, methodologies change, and staying competitive requires continuous investment in your skills. “But when do I find time to learn if I work full-time?” The answer is simple: in your personal time and systematically. This investment in yourself will drive your career forward. Here are some tips on how to make time and stay motivated for self-development.

Planning – the key to effective learning

The older we get, the less free time we have. However, it’s crucial to carve out even a small window for learning. To make the most of your time, you need a plan. Set a goal – for example, learning a new technology – and break it down into tasks. Spend at least 30-60 minutes daily working on your plan. This will help you track progress and stay motivated. Think of your personal growth as a project you are actively developing. Good planning will help you succeed more efficiently and quickly.

Practice – the best way to reinforce knowledge

Whether you’re learning a new programming language, technology, or even English, practice is essential. Theory without real-world application fades fast. Programming is like working out in a gym: the more repetitions, the better you remember and understand. Regularly solve problems, rewrite algorithms, and engage with the code. Even mastering keyboard shortcuts or design patterns will significantly boost your productivity.

Why dive deeper into technologies?

We often ignore knowledge that seems unnecessary. For instance, if you use Laravel Eloquent ORM, you might think knowing raw SQL isn’t essential. Or if you rarely write bash scripts, learning their syntax might seem pointless. But technologies change rapidly, and understanding things at a deeper level makes adaptation easier. The more you know about how things work under the hood, the more effectively you can debug issues and find optimal solutions.

Why learn other programming languages?

If you work with PHP, exploring other languages will expand your perspective and improve your coding approach. For example, integrating Laravel Octane (with RoadRunner or Swoole) changes how the server operates, making it stateful like Java, Node.js, or Golang. If you don’t understand how stateful servers work, adapting will be difficult. Learning a new programming language isn’t a waste of time — it’s an investment in your flexibility and professional growth.

Laravel Pipeline: A Powerful Tool for Managing Data Flows

Andrew Dabych — Sun, 02 Mar 2025 22:00:00 +0000

What is Laravel Pipeline and How Does It Work?

Pipeline is an implementation of the Pipeline Pattern, which allows an object to pass through a series of handlers, each performing a specific task. This is a great way to structure code following the Single Responsibility Principle (SRP), which is part of SOLID.

Key Features of Pipeline

Pipeline is especially useful in scenarios like:

Object transformation – when you need to apply multiple handlers to modify or enrich an object.
Model processing before saving or after updating – when you need to execute several independent processes, such as creating relationships, sending notifications, or integrating with third-party services.

Example of Laravel Pipeline Usage

Let's say that after creating a user, we need to:

Generate a profile picture;
Activate a subscription;
Send a welcome email.

Instead of handling this logic in one place, Pipeline can be used:

use Illuminate\Pipeline\Pipeline;

$user = Pipeline::send($user)
            ->through([
                GenerateProfilePhoto::class,
                ActivateSubscription::class,
                SendWelcomeEmail::class,
            ])
            ->then(fn (User $user) => $user);

Each handler is a separate class, like this:

class GenerateProfilePhoto {
    public function handle(User $user, Closure $next) {
        // Generate profile photo
        $user->profile_photo = 'default.png';

        return $next($user);
    }
}

The key takeaway: each class receives the $user object, performs its logic, and passes it to the next handler using $next($user). This is similar to Middleware but can be used beyond HTTP requests, in any business logic.

Why Use Pipeline?

Cleaner and more readable code – each process is in its own class.
Flexibility – easily add or remove handlers.
Reusability – the same classes can be used in different parts of the application.

Additional Resources

🔗 Laravel Pipeline Documentation

Conclusion

If you need to process data sequentially, Pipeline is a great way to do it efficiently and elegantly. Now that this tool is officially documented in Laravel 10, using it is easier than ever.