5 Times LLMs Help You Code… and 5 Times They Fail

Stack Overflow’s survey shows devs don’t fully trust AI – yet they can’t stop using it! While agentic AI remains mostly hype, LLMs continue to dominate everyday developer workflows.

The survey shows GPT models dominate, with 82% of developers using them last year. Claude Sonnet is gaining ground too – especially among pros (45%) over beginners (30%). While many try “vibe coding,” 77% say it’s not part of their real workflow.

This gap between use and trust drove us to explore where LLMs truly help and where they can make a mess. To break it down, we put together a list of the top 5 good ways to use these AI tools, along with 5 situations where you should absolutely steer clear.

LLMs as superstars for these tasks

LLMs excel at practical, time-saving tasks – particularly those that are repetitive or well-structured. From generating boilerplate code and debugging to drafting docs or translating languages, they act as tireless productivity boosters. They’re also handy learning companions, offering explanations and exercises on demand.

Next, we’ll highlight where LLMs truly add value in everyday development.

1. Boilerplate and setup

We all know that LLMs solve repetitive tasks excellently. That happens because they can automatically generate a project template, initialization code, config files, or standard classes and functions in just one second.

For example, ChatGPT needs only one second to hit the usual patterns and syntax on the first attempt. They are most effective for generating base code, which the developer then reviews, shapes, and adapts.

2. Debugging assistance

LLMs always step in as reliable assistants when you need “another pair of eyes” to find a bug. They analyse code surprisingly well because they can spot logical gaps, propose fixes, and even explain why a function fails specific tests.

For example, you only need to insert an error message and ask, “What is wrong here?” – the model will suggest where the issue hides. Of course, always check suggestions through tests.

3. Documentation and comments

Writing documentation and comments often feels boring, but LLMs do it quickly and efficiently. They can generate docstrings, README sections, or comments based on code.

As one Reddit user says: “for simple tasks – like adding documentation or looking up syntax – I can get a near-instant solution.” So, in this case, LLMs are excellent for filling documentation gaps or creating explanations for colleagues who need help.

4. Code conversion

Porting code between languages or frameworks often feels exhausting. ChatGPT can help translate a Python function into Java or C# code. It works best with popular languages and standard patterns.

Naturally, you always need to validate the translated code and adapt idioms or APIs, but as a first step, it can significantly speed up the process.

5. Learning aid

Naturally, the thing we all use LLMs for: quick learning! They can serve as an educator available 24/7. They can explain concepts, clarify syntax, or offer exercises.

For example, you can ask ChatGPT: “What does this error mean?” or “Give me a recursion exercise for beginners.” The model will generate explanations and examples that align with your knowledge level.

Again, this proves very useful for self-study, with the note that you must verify every claim.

Where LLMs consistently fail

LLMs aren’t perfect. They can speed up development, but real-world tasks reveal their limits – from subtle bugs and insecure code to flawed designs and privacy risks. Over-reliance can even weaken core developer skills.

Next, we highlight where LLMs often fall short and the risks teams should watch for.

1. Risks in production code

Do not assume that AI code works for production. Even when it looks correct, it can contain hidden bugs or incomplete logic.

Experienced developers warn: always “double-check your code, run the appropriate tests, and use your best logical judgement.” Also, keep in mind that models act non-deterministically—the same prompt can yield different results.

2. Security vulnerabilities

AI often suggests insecure patterns. Research shows that about 40% of Copilot suggestions contained exploitable vulnerabilities.

ChatGPT can generate similar problems (e.g., SQL injection, weak encryption). Best practice dictates treating every AI-generated code as insecure until you check it with static analysis and a code review process.

3. Limitations in system design

LLMs do not suit the creation of architecture or complex systems. They can assemble a diagram or deployment script, but lack an understanding of context and trade-off decisions.

They can also suggest outdated libraries or naive choices. Use them only for smaller parts of the design you control and verify.

4. Data privacy issues

Stay careful with what you send to LLM services. Prompts often get logged and stored. That means your code may end up in a future training set.

Experts warn that uploading source code into a public model can “inadvertently disclose vital trade secrets.” Never input confidential data (passwords, internal comments, user information) into public models.

5. Skill degradation

Excessive reliance on LLMs can cause developers to neglect fundamental skills. Junior developers skip learning basics, while seniors lose the habit of diving deep into problems.

As analyses warn, “skills atrophy” is a real risk. Over time, teams may code quickly but stay underprepared for unexpected issues. Best practice suggests using LLMs as assistants and tutors, not as crutches.

LLMs serve as helpful assistants but never as infallible ones. Treat them best “like Stack Overflow snippets: useful, but unreliable.”

Take advantage of the “yay” areas to accelerate routine tasks, but always keep the “nay” risks in mind. With careful use and human oversight, LLMs can help you increase productivity without breaking your codebase.