DEV Community: Randy Letona

AI Coding - Best Practices in 2025

Randy Letona — Fri, 19 Sep 2025 01:32:07 +0000

Artificial Intelligence has rapidly become a daily coding companion. Tools like GitHub Copilot, ChatGPT, Claude, and Gemini CLI are transforming how we write, debug, and maintain software. With the rise of agent modes inside IDEs such as Visual Studio Code, developers can now code faster than ever—but speed doesn’t always mean quality.

The truth is, AI is not a replacement for engineering discipline. It’s a powerful accelerator, but if used carelessly it can also introduce logical inconsistencies, bugs, and poor architectural choices. This article covers best practices for coding with AI assistants, ensuring that you get the productivity boost without sacrificing maintainability or correctness.

1. Treat AI as a Pair Programmer, Not an Autopilot

It’s tempting to accept the first suggestion Copilot or ChatGPT throws at you. Don’t.
Instead, think of AI as a junior developer sitting beside you: it can produce drafts, boilerplate, or even surprisingly solid algorithms, but it lacks the deep context of your project.

Best practice:

Always review generated code line by line before committing it.
Ask yourself: Does this logic align with the existing architecture? Is it handling edge cases?
When in doubt, test it immediately.

2. Use the Right Tool for the Right Context

Different AI tools shine in different workflows:

GitHub Copilot – great for inline code suggestions, boilerplate generation, and repetitive tasks.
Copilot Agent in VS Code – powerful when you want to query your codebase, refactor large chunks, or explore relationships between files.
ChatGPT – better for architectural advice, debugging explanations, and documentation drafts.
Claude – excels at long-context reasoning, analyzing big files or entire repositories without losing track.
Gemini CLI – a good option for terminal-based workflows, quick prototyping, or scripting assistance.

Best practice:
Match the AI to the job. Don’t expect Copilot to architect your microservices, and don’t use a chat model for one-line regex completions.

3. Keep a Tight Feedback Loop

One common mistake is generating large chunks of code, pasting them in, and hoping they’ll “just work.” This usually creates hidden errors, broken dependencies, or missed edge cases.

Best practice:

Generate in small increments.
Run unit tests after each integration.
Use version control aggressively—commit frequently so you can roll back if AI suggestions take your project in the wrong direction.

4. Guard Against Logical Errors

AI assistants are notorious for producing code that looks right but hides logical flaws. For example, they might write a sorting function that works on most cases but breaks with duplicates or edge values.

Best practice:

Write tests before integrating AI-generated functions (TDD mindset).
Ask AI explicitly: “What are the possible edge cases?” or “Show me test cases that could break this function.”
Run linters and static analyzers (ESLint, Pylint, SonarQube, etc.) on all generated code.

5. Prevent “Code Drift”

One of the biggest dangers with AI tools is inconsistency. You may end up with functions that follow different naming conventions, error-handling strategies, or architectural patterns—depending on what mood the model was in that day.

Best practice:

Define project-wide standards and style guides (naming, error handling, comments, security).
Feed those standards back into your AI prompts. Example: “Write this function using our project’s async/await error handling convention with centralized logging.”
Regularly run formatting tools (Prettier, Black, gofmt) and enforce them with CI/CD.

6. Never Outsource Security Thinking

AI can introduce subtle security flaws: unsafe SQL queries, weak password hashing, or bad JWT handling. Models may not be aware of the latest CVEs or compliance standards.

Best practice:

Always review AI-generated code for injection risks, hardcoded secrets, and insecure defaults.
Use security linters (Bandit, npm audit, dependency-check) in your pipeline.
Keep security-sensitive logic (auth, encryption, payments) under closer human review.

7. Document as You Go

AI can write documentation, but it often generates generic comments that don’t reflect your actual reasoning. Documentation is most valuable when it captures why you made certain decisions.

Best practice:

Use AI for first drafts of docstrings, READMEs, or inline comments.
Then refine them with your own reasoning and project-specific details.
Keep docs updated alongside the code—otherwise AI-generated code quickly goes out of sync.

8. Ask for Explanations, Not Just Code

One overlooked use case: you can ask Copilot Agent, ChatGPT, or Claude to explain what the generated code does. This can highlight potential errors you missed.

Example prompt:

“Explain this function step by step. What assumptions is it making? Could it fail with certain inputs?”

Often, the explanation reveals hidden assumptions or limitations that weren’t obvious from just skimming the code.

9. Don’t Skip Human-Led Code Reviews

No matter how much AI you use, peer review remains essential. Another developer’s perspective will catch inconsistencies, questionable design choices, or readability issues that AI won’t flag.

Best practice:

Treat AI-generated code the same as human-written code: require PR reviews.
Encourage reviewers to check not just for correctness but also for long-term maintainability.

10. Embrace Continuous Learning

AI coding tools evolve weekly. Copilot’s agent mode, Gemini’s command-line features, and Claude’s expanded context windows are just the start.

Best practice:

Stay updated on new capabilities, but don’t adopt them blindly.
Continuously evaluate how each tool impacts your velocity, quality, and team workflow.
Share learnings internally: document best prompt patterns, workflows, and pitfalls.

Final Thoughts

AI-assisted coding isn’t about replacing engineers—it’s about augmenting them. The best developers of 2025 will not be the ones who generate the most lines of code with AI, but the ones who know when to trust it, when to question it, and how to integrate it responsibly.

By treating AI as a partner, enforcing standards, testing relentlessly, and keeping human judgment at the core, you can unlock the full potential of tools like Copilot, ChatGPT, Claude, and Gemini CLI—while still building software that is secure, consistent, and maintainable.

Methodology: 12 Factor apps

Randy Letona — Fri, 04 Apr 2025 16:50:15 +0000

Hey Devs,
There are many methodologies out there for building software, each suited to different stages or goals. One methodology that stands out for building robust, scalable, and maintainable applications—especially in cloud-native environments—is the 12 Factor App.

When starting a project, whether it’s an MVP or a full-fledged product, it’s crucial to have a clear plan—a foundation that sets the project up for long-term success. The 12 Factor methodology provides a practical checklist for structuring an application properly from day one. Regardless of the technology stack or framework you use, these principles help ensure your app is ready to scale and adapt over time.

1. Codebase

There should be one codebase tracked in version control (like GitHub, GitLab, Bitbucket), and many deployments. All environments—development, staging, production—should derive from a single source of truth.

🔁 “One app, one repo.”

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2. Dependencies

All external dependencies must be explicitly declared in manifest files (like package.json, pyproject.toml, etc.), and not assumed to be installed on the system.

📦 Use dependency managers (npm, yarn, pip) to install and isolate packages.

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3. Config

Configuration such as API keys, database URLs, and client secrets should be stored in environment variables, not hardcoded in the codebase. This allows easy configuration changes per environment (dev, staging, prod).

🔐 Environment-driven configuration keeps your app flexible and secure.

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4. Backing Services

Treat all services your app consumes—like databases, queues, caches, storage—as attached resources. They should be easily swappable without code changes, referenced via URLs or credentials.

🔄 Replace your database or S3 bucket without touching the code.

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5. Build, Release, Run

Separate the stages of:
• Build: Compile the code and install dependencies.
• Release: Combine the build with configuration for a specific environment.
• Run: Execute the app in the runtime.

🔧 This ensures predictable, repeatable deployments.

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6. Processes

Your application should run as stateless processes. Any data that needs to persist should be stored in a backing service (like a database or object storage).

☁️ Restarting your app should not affect its state or integrity.

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7. Port Binding

The app should self-host by binding to a port, and not rely on external servers (like Apache or Nginx) for communication.

🌐 For example, in Node.js: app.listen(process.env.PORT || 3000);

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8. Concurrency

Design your app to scale out by running multiple instances or processes for different tasks (e.g., web server, background worker, scheduler).

⚙️ Use process managers (PM2, Docker, Kubernetes) to scale horizontally.

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9. Disposability

Apps should start up and shut down quickly and gracefully, allowing for fast scaling and reliable deployments.

⏱️ Handle termination signals (like SIGTERM) to close resources cleanly.

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10. Dev/Prod Parity

Keep development, staging, and production environments as similar as possible to avoid bugs and deployment issues.

🐳 Tools like Docker are ideal to ensure environment parity.

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11. Logs

Treat logs as event streams. Your app should write logs to stdout/stderr, and let external tools (like ELK, Sentry, or Datadog) handle log aggregation and analysis.

📊 Don’t manage logs with files inside the app.

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12. Admin Processes

Administrative tasks like database migrations, data seeding, or one-time scripts should be run as isolated one-off processes.

🛠️ Example: npx prisma migrate deploy or rails db:migrate

Final Thoughts

The 12 Factor App methodology isn’t a strict rulebook—it’s a set of guiding principles that help you build better apps. Whether you’re working on a monolith, microservices, or serverless architecture, applying these concepts will significantly improve your application’s resilience, scalability, and maintainability.