Ever sat in a coding interview, staring at a problem while thinking, "I wish I could just ask ChatGPT for help right now"? You're not alone. Thousands of developers are secretly wondering if using LLMs during interviews is brilliant strategy or career suicide.
Let me save you hours of ethical debate: there's a right way and a wrong way to leverage LLMs in coding interviews, and knowing the difference could land you that dream job.
The smart use of LLMs as interview preparation tools can dramatically improve your problem-solving skills and coding confidence without crossing ethical lines.
But here's what most candidates get completely wrong about using AI in their interview prep – and it's costing them offers at top tech companies...
Large Language Models like ChatGPT, Bard, and Claude aren't just fancy chatbots—they're coding companions with serious skills. These AI systems have been trained on billions of lines of code across numerous programming languages, making them surprisingly good at:
Think of them as the ultimate pair programmer who never gets tired or hungry (though they do hallucinate occasionally).
The real magic happens when you're stuck on a problem. These models can help you break down complex coding challenges into manageable steps, suggest data structures, or remind you about edge cases you might've missed.
Got ethics? You should when it comes to LLMs in coding interviews.
Most companies have mixed feelings about AI assistance during technical assessments. Why? Because interviews aim to evaluate YOUR skills, not an AI's capabilities.
Using LLMs without disclosure is generally considered dishonest. It's like bringing hidden notes to an exam—technically cheating.
Some ethical approaches include:
Remember that many companies now use plagiarism detection tools specifically designed to catch AI-generated code.
LLMs are impressive, but they're not coding wizards who'll magically ace your interview for you.
What they're great at:
What they struggle with:
The bottom line? LLMs make excellent study partners and coding assistants, but they can't replace the deep understanding you need to shine in a coding interview. You still need to understand what the code does and why it works.
Getting the right LLM for your coding interview prep can make or break your experience. Not all LLMs are built the same.
ChatGPT might be your go-to choice, but have you considered GitHub Copilot for real-time coding suggestions? Or Anthropic's Claude for its longer context window when you're working through complex problems?
What you need depends on what you're practicing:
Don't just stick with the free version if you're serious. The paid tiers often give you access to more powerful models that understand complex coding concepts better.
Your prompt is your superpower. Garbage in, garbage out - this is especially true with LLMs.
The difference between:
"How do I solve this array problem?"
And:
"I need to find duplicates in an unsorted array with O(n) time complexity. Here's my current approach: [code]. Where am I going wrong?"
The second will get you actual help instead of generic advice.
Some prompt patterns that work wonders:
The magic isn't in random practice but in consistency and progression.
A solid schedule might look like:
Track your progress in a spreadsheet or Notion. Note which problems you solved with heavy LLM assistance versus independently. This creates accountability.
Set clear goals: "This week I'll solve 10 medium-difficulty problems with decreasing LLM help."
The trap many fall into? Over-reliance on AI help.
Think of LLMs as training wheels - helpful at first, but eventually you'll need to ride without them.
Try this approach:
Remember why you're using LLMs - not to outsource your thinking but to accelerate your learning. The interviewer wants to see YOUR problem-solving skills, not ChatGPT's.
Ever stared at an algorithm description feeling completely lost? That's where LLMs shine. Ask them to break down complex algorithms like Dijkstra's or Quick Sort in simple terms.
Say you're asked about A* pathfinding in an interview. Just prompt: "Explain A* algorithm as if I'm a junior developer." The LLM will give you a clear explanation without the intimidating mathematical notation you'd find in textbooks.
The real magic happens when you follow up with questions like "What's the intuition behind the heuristic function?" This helps you grasp the why, not just the how.
Coding problems rarely have just one solution. LLMs can show you different ways to tackle the same challenge.
Try this: "Show me three different approaches to solve the two-sum problem." You'll get everything from the brute force O(n²) solution to the optimized hash map approach in O(n).
This gives you a massive advantage in interviews where demonstrating solution flexibility scores major points.
Big O notation tripping you up? LLMs can demystify complexity analysis.
Instead of memorizing formulas, ask: "Analyze the time and space complexity of this merge sort implementation." Then follow with "How would this change if I modified step X?"
What's cool is you can paste your own solution and get immediate feedback: "Is this implementation O(n log n) or O(n²)? Why?"
Interviewing at a company that uses a language you're rusty with? LLMs are your multilingual coding companions.
If you know the solution in Python but need it in Java:
"Convert this Python function to Java while maintaining the same logic:
def merge_intervals(intervals):
# code here
```"
They'll translate not just the syntax but also language-specific idioms and best practices.
Computer science fundamentals form the backbone of technical interviews. LLMs help you refresh these concepts quickly.
Confused about the difference between processes and threads? Ask: "What's the key difference between processes and threads, and when would you choose one over the other?"
You can even simulate interview questions: "How would you explain virtual memory to a junior developer?" This preps you to articulate complex concepts clearly—a skill interviewers love.
Coding interviews can feel like you're being asked to climb Mount Everest in flip-flops. The secret? Don't look at the whole mountain. Break it down.
Here's how LLMs can help:
For example, if you're asked to build a social media feed algorithm, ask your LLM: "What are the main components I need to consider for a social media feed algorithm?"
The magic happens when you turn overwhelming problems into bite-sized chunks that your brain can actually process.
We've all been there. Your mind goes blank. The interviewer is waiting. Panic sets in.
LLMs are your secret weapon against this nightmare scenario. When you hit a wall:
Don't ask "How do I solve this entire problem?" Instead try: "I'm trying to implement a binary search but can't figure out how to handle the edge cases. What should I consider?"
The goal isn't to have AI code for you—it's to nudge you in the right direction.
Try these prompts:
The key difference between cheating and smart preparation is how you engage with the AI. Use it to understand concepts, not to memorize solutions.
Your first solution probably won't be perfect. That's normal.
Use LLMs to polish your code by asking:
Each prompt builds on the previous one, gradually improving your solution. Think of it as having a conversation with a mentor, not getting answers from a calculator.
Ever stared at your code for hours and missed that obvious bug? LLMs can be your second pair of eyes during coding interviews. Simply paste your solution and ask: "Can you check this for bugs or edge cases I might have missed?"
They're particularly good at spotting:
When I recently solved a binary search problem, my LLM buddy pointed out I hadn't handled empty arrays. That would've been an instant red flag to the interviewer!
Your code works, but is it good? Ask your LLM to optimize it:
Can you suggest ways to make this code more efficient or readable?
The AI might recommend:
Different solutions have different trade-offs. LLMs excel at explaining these:
What are the space-time trade-offs between my solution and alternatives?
This helps you sound thoughtful when the interviewer asks "Could you have solved it another way?"
Show the interviewer you write professional-quality code by asking:
Does this follow best practices for [language]?
The AI will suggest improvements like:
Just remember - the goal isn't to have the LLM write perfect code for you, but to help refine what you've already written. Interviewers value your thought process more than syntactic perfection.
Want to ace your coding interview? Start practicing with an LLM as your interviewer. It's super easy to set up.
First, craft a clear prompt like: "You're a senior developer interviewing me for a backend role. Ask me a medium-difficulty question about data structures, then evaluate my solution."
The magic is in the details. Tell the LLM:
Here's a game-changer: feed the LLM real interview questions from sites like LeetCode or actual company interviews you've heard about. This makes the simulation feel eerily authentic.
The brutal truth? Your coding buddies might be too nice. LLMs aren't.
When you submit your solution, ask the LLM to evaluate it from multiple angles:
Don't just take the first feedback round. Push deeper with follow-ups like "How would you optimize this further?" or "What would a senior engineer do differently?"
Coding is only half the battle. You need to explain your thinking clearly.
Type out your thought process exactly as you'd say it in an interview. Ask the LLM to evaluate not just what you said, but how you said it:
Try this: Record yourself explaining a solution, transcribe it, and have the LLM critique your communication style. This catches those "ums" and circular explanations that real interviewers notice.
Interviews are stressful. Recreate that sweaty-palms feeling.
Set a timer for 20-30 minutes before you start. Tell the LLM: "Only give me hints if I explicitly ask, and remind me of the time remaining every 5 minutes."
For extra realism, try these pressure-cookers:
The best part? You can fail spectacularly in these simulations with zero consequences. Each failure is just preparation for the real thing.
Bombing a coding interview hurts. But there's gold in those failures if you know where to look.
Pull up your LLM of choice and feed it your interview questions - especially the ones that tripped you up. Ask it something like: "What patterns do you notice in these five problems I struggled with?"
You might discover you consistently miss dynamic programming questions or struggle with tree traversals. LLMs excel at spotting these patterns because they've seen thousands of coding problems and can identify what makes them similar.
For example:
Me: I failed these questions: [list of problems]
LLM: I notice you struggle with recursion, particularly when the base case isn't obvious.
Once you know your weak spots, it's time to target them specifically. Don't waste time studying everything.
Ask your LLM to build you a personalized roadmap:
Create a 3-week study plan focusing on graph algorithms with daily practice problems of increasing difficulty.
The beauty here? Unlike static courses, your LLM-created curriculum adapts. Had a breakthrough with BFS but still struggling with Dijkstra's? Just tell it, and watch your plan adjust in real-time.
The explanations you get from LLMs during practice sessions are too valuable to lose. Create a personal wiki or Notion database where you store these insights.
For each concept you struggle with, save:
Progress in interview prep can feel invisible until you measure it.
Have your LLM generate similar problems to ones that stumped you before. Then tackle them again every few weeks. Record your solving times, whether you needed hints, and your overall confidence level.
You can even ask the LLM to evaluate your solutions over time:
Compare my solution from two weeks ago to the one I just wrote. What specific improvements do you see?
This concrete evidence of growth is rocket fuel for your motivation.
Mastering the use of Large Language Models in coding interviews can transform your preparation and performance. By understanding LLMs' capabilities, preparing strategic prompts, and using them for concept clarification and problem-solving guidance, you can enhance your technical skills and confidence. These tools become especially valuable when reviewing code for optimization and simulating interview scenarios to practice your responses.
Remember that LLMs should complement—not replace—your own critical thinking and coding abilities. Use them as partners in your learning journey, helping you to identify knowledge gaps and build stronger problem-solving muscles. As you continue to grow as a developer, incorporate these AI assistants into your regular practice routine, and you'll approach your next coding interview with greater skill and assurance.
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