Large Language Models have revolutionized how we approach software development, offering unprecedented assistance in coding, debugging, and problem-solving.  As developers increasingly integrate AI tools into their workflows, it’s crucial to understand how to use them safely and effectively. 

Here are some tips that can help you along the way. These tips aren’t just theoretical guidelines; they’re practical lessons learned from real-world development scenarios where AI assistance can either accelerate your work or create serious problems if not handled properly. 

All tips are deeply interconnected, but if I had to pick one, it would be “Maintain Your Skills.” Think of this one as the base of all tips you’ll ever get, and all other ones will always navigate back to it. 

Maintain Your Skills 

This is by far the most important aspect of safe LLMs coding. You need to know its limits, yes, but you also need to expand and maintain your own skills as a software developer. 

Don’t become overly dependent on LLMs. Continue learning and practicing fundamental programming concepts. Use AI as a tool to enhance your capabilities, not replace your understanding of core development principles. 

With the introduction of powerful language models, it’s extremely important to expand your current knowledge. You should use them not only for problem-solving but also as a teaching tool. 

Although fears persist about AI displacing programmers and other professionals, the technology is actually driving demand for these roles with updated skill requirements. 

The smaller the gap in your knowledge, the better the solution. The more you understand about the problem domain, programming concepts, and best practices, the better you can evaluate and refine LLM suggestions. Your existing knowledge acts as a quality filter, helping you spot potential issues, security vulnerabilities, or inefficient approaches before implementing them. 

Context is King 

Always provide comprehensive background information, project requirements, and constraints when asking for help. The more specific context you give about your codebase, architecture, and goals, the better the LLM can tailor its suggestions to your actual needs rather than generic solutions. 

It’s best to assume LLMs have limited knowledge and that every piece of information shared will help them and you work toward a common goal. 

Prompts like “Can you create a web form for whatever…” are good only for presentations or YouTube videos. Your web stack certainly already has lots of dependencies, so you should include them in your prompt.  

“Create a Python Flask web form for user signup; use React and Tailwind.”

This sounds far better, and if you’re stuck, you can always ask the LLM to help you refine your prompt. 

Even for simple Linux tasks, you should state your preferences like shell, tools to use (awk, grep, jq), and so on. If you miss context, some models will usually provide you with multiple solutions, but you shouldn’t count on this. Always add as much information as you find relevant. 

Trust No One 

Never blindly copy-paste LLM-generated code into production. Always review, test, and understand every line. LLMs can produce plausible-looking code that contains subtle bugs, security vulnerabilities, or logic errors that could cause serious issues. 

The internet is full of cautionary stories—most are not as extreme as “AI deletes entire database during code freeze,” but they’re warnings, nonetheless. This is why you should always double-check any LLM output or command before executing. Fortunately, a lot of modern tools, like Claude Code, always ask for permission to modify or execute any command, so nothing would run automatically. 

Sometimes, problems are subtle changes to your code logic during refactoring or similar activities. They might remove one condition that, at first glance, seems minor, but it exists for some specific reason relevant to your environment. Remember, they are trained mostly on public code, and your environment can have lots of specific requirements. 

Follow Industry Trends 

Stay updated with the latest advances in AI-assisted development, security best practices, and emerging tools. The LLM landscape evolves rapidly, and new safety guidelines, limitations, and capabilities are discovered regularly. 

Here are some useful resources: 

  • YouTube with specific channels like Anthropic AI, ThePrimeTimeagen, and TechnoVangelist—the algorithm will suggest new content when you watch and like a few of them.
  • Hugging Face (https://huggingface.co/) is a machine learning platform and community often called the “GitHub of AI.” 
  • Simon Willison’s Weblog (https://simonwillison.net/) – the maker of my favorite CLI interface (https://llm.datasette.io/en/stable/) and a great source for AI news. 

By following any of these suggestions, you’ll discover plenty of other resources worth following up on. 

Familiarize Yourself with Models 

Understand the strengths, weaknesses, and training cutoff dates of different LLMs. Each model has different capabilities—some excel at certain programming languages or domains while struggling with others. Know which model to use for which task. 

If you follow links from industry trends, there will be plenty of articles and benchmarks about models, their internals, strengths, and weaknesses. You can also look at benchmarks like Gorilla Leaderboard (https://gorilla.cs.berkeley.edu/leaderboard.html), Aider leaderboards, or follow releases from tools specialized in running local LLMs, like Ollama and Hugging Face. Every model page usually has plenty of documentation from creators, and sites like OpenRouter provide rankings. 

Currently, my preferred models for coding are from the Claude family, specifically Claude Sonnet 4. 

Experiment with Different Implementations 

Don’t settle for the first solution an LLM provides. Ask for alternative approaches, compare different methods, and test multiple implementations. This helps you understand trade-offs, discover more efficient solutions, and avoid over-reliance on a single potentially flawed approach. 

Always check and double-check every step or action that will be taken. 

Use “Explain” Whenever in Doubt 

If you don’t fully understand generated code, ask the LLM to explain its logic, potential edge cases, and assumptions. Understanding the “why” behind the code is crucial for maintenance and debugging. 

Bonus Tips

💡Cross-reference with official documentation: Verify LLM suggestions against official documentation, established best practices, and trusted sources. LLMs can sometimes provide outdated information or misinterpret API usage. 

💡Maintain version control and incremental changes: Implement LLM suggestions in small, tracked commits so you can easily revert problematic changes and maintain a clear development history. 

One Last Thought… 

LLMs are powerful tools that can significantly accelerate development, but they require careful handling to avoid costly mistakes. Always remember to experiment with different approaches, ask for explanations when uncertain, and implement changes incrementally with proper version control. 

The goal isn’t to avoid AI tools, but to use them intelligently while preserving the critical thinking and technical understanding that make you an effective developer.