When ChatGPT first came out, developers were terrified. 'Coding is dead.' I was scared too. But after integrating LLMs into production for a year, I realized: AI is not a God, but an incredibly smart intern who sometimes hallucinates confidently.
Both are children of Transformer, so why the difference? Using 'Fill-in-the-blank' vs 'Write-next-word' analogies to explain BERT vs GPT. Practical guide based on trial and error.
ChatGPT answers questions. AI Agents plan, use tools, and complete tasks autonomously. Understanding this difference changes how you build with AI.
I actually used all three AI coding tools for real projects. Here's an honest comparison of Copilot, Claude Code, and Cursor.
My AI chatbot was hallucinating wild answers to customers. Here's how I implemented RAG (Retrieval-Augmented Generation) to fix it, covering Vector DBs, Embeddings, and Hybrid Search.
In November 2022, when ChatGPT was released, my first emotion was 'Fear'. The Regex pattern I spent 3 hours sweating over, it spit out in 3 seconds. And its code was cleaner than mine.
"Ah, my livelihood is gone. Should I learn how to fry chicken?"
But a year later, I am not a chicken shop owner, but a Developer who exploits AI. Here is how I overcame the fear and accepted it as a tool.
At first, it was magical. "Create a Todolist in React," and boom, it's done. "Refactor this," and it cleans up variable names beautifully.
But soon I hit a Wall.
I asked it to use the latest version of a library, and it confidently created a non-existent function (createSuperButton()). (This is called Hallucination.)
I copied and pasted it, and of course, it crashed. When I complained, it apologized and invented another fake function.
Then I realized.
"Ah, this isn't a genius. It's a parrot specialized in sounding plausible."
Realizing this actually relieved me. Judgment (Validation) was still the human's job.
To use LLM properly, you must understand how it works. It is not a thinking machine. It is a "Machine predicting the next word based on probability."
Input: "The capital of Korea is"
Model:
- "Seoul" (99.8%)
- "Busan" (0.1%)
- "Washington" (0.01%)
It just learned from massive text that "Seoul" likely follows "Capital of Korea". That's why it sometimes tells lies that are logically impossible but grammatically perfect.
If you ask ChatGPT which is larger, 9.11 or 9.9, it sometimes says 9.11.
Not because it's stupid, but because it sees numbers as 'Tokens'.
If it sees 9.11 as 9, ., 11 and 9.9 as 9, ., 9, then 11 is greater than 9.
You must understand this limitation of Tokenization to not get frustrated by its nonsense.
LLMs have a setting called Temperature (0 to 1).
Ideally, we want Temperature=0 for coding. But typical chat interfaces mix this up.
So explicit prompting like "Kill the creativity (Temperature=0) and give me facts only" helps.
Understanding this changes your attitude. Not "Give me the answer," but "Write a draft that I can verify."
Now I can't live without AI. But I never let it write code from scratch to finish.
Syntaxes that are tedious and error-prone for humans. "Write a regex to validate emails," "Write SQL to find users who didn't pay last month." AI is much better at this. (Testing is mandatory, though.)
"What should I name this function?" It gives decent suggestions. Throw complex legacy code at it and say "Explain this," and it drastically reduces analysis time.
"Write edge case tests for this function." It finds exceptions I missed (null input, empty arrays) and writes tests for them.
The difference between a dev who uses AI well and one who doesn't lies in the Prompt.
[Novice]"Write a login feature." (Result: Messy code full of security vulnerabilities)
"Use Next.js 14 App Router and NextAuth.js to implement social login. Store tokens in httpOnly cookies, and define types strictly with TypeScript." (Result: Production-ready code)
If you ask for the answer immediately, AI gets dumb. If you tell it to "Think step by step", it gets smarter.
[Bad Prompt] "Find bugs in this code."
[Good Prompt] "You are a Senior Engineer.
Forcing this thought process significantly reduces hallucinations.
AI is excellent at handling the 80% of boring and repetitive tasks. But the remaining 20% of core business logic and architectural decisions are still up to humans.
A common mistake juniors make is trying to make AI do 100% of the work. If you try to get 100% from AI, you will spend 200% of the time debugging later. AI is a 'Copilot', not the 'Pilot'. You must always keep your hands on the yoke. This rule applies not only to coding but also to documentation, emails, and meeting notes.
Even in the AI era, only developers who know "What to build" and "How to build" survive. You can't ask the right questions if you know nothing.
AI is comfortable. But comfort comes with a price. That price is the 'Decline of Code Literacy'.
When we write code ourselves, we agonize over every line. But when AI spits out 100 lines, we tend to glaze over it and say "Looks about right." If this repeats, your ability to "Read Code" atrophies.
The role of a Senior Developer is shifting from 'Writer' to 'Reviewer'. And as we all know, reviewing someone else's code is 10x harder than writing your own. If you cannot verify AI's code as thoroughly as your own, you are just a Ctrl+C, Ctrl+V machine.
"Do we need juniors when we have AI?" Half right, half wrong. Juniors who "don't know why AI's code works" will be obsolete. But Juniors who "use AI to produce like seniors" will have massive opportunities.
Developer competence is no longer "Memorizing syntax" but "Problem Solving" and "AI Handling".
Don't be afraid. Ride it. AI isn't here to take your job, but to bring your clock-out time earlier.