So, you’ve been playing around with GPT-5, & it’s pretty mind-blowing, right? The model is an absolute powerhouse for coding. OpenAI's own announcements from August 2025 shouted from the rooftops about its state-of-the-art performance, its ability to handle complex tasks, & its prowess on coding benchmarks. It's being integrated into everything from Microsoft products to developer tools like JetBrains. Honestly, it feels like a genuine coding collaborator.
But here’s the thing. Sometimes, that power can be a little…much.
You ask it for a simple script, & it gives you a multi-layered, enterprise-grade solution that, while technically impressive, is like using a sledgehammer to crack a nut. You wanted a simple function, & you got a symphony of classes, decorators, & design patterns you have to spend an hour untangling. It’s a classic case of the AI complexity trap.
If you're nodding along, don't worry. You're not alone. The challenge with these incredibly powerful models isn't just getting them to produce code; it's getting them to produce the right code—simple, readable, & maintainable. Turns out, reining in the complexity is a skill in itself.
Let's dive into how you can steer GPT-5 away from the scenic route & get it to write clean, simple code that actually makes your life easier.
It All Starts with the Prompt: The Art of Being a Good Boss
Working with GPT-5 is like being a manager. If you give vague instructions, you’re going to get some… creative interpretations. To get simple code, you need to be an exceptionally clear & direct boss. Prompt engineering isn't just a buzzword; it's the instruction manual for your AI.
The core idea is to be specific, descriptive, & as detailed as possible about what you want. Don't just say "make a to-do list app." That's an open invitation for complexity. Instead, think about the simplest possible version of that app & describe it.
Foundational Techniques: Getting the Basics Right
Before we get into fancy tricks, let's cover the bedrock of good prompting. These are the simple, direct methods that should be your first port of call.
1. Direct Instruction Prompting
This is the most straightforward approach. You give a clear, concise command without a lot of fluff. The key is to strip out ambiguity.
Instead of: "Write a Python script for a to-do list."
Try: "Write a simple Python command-line script. The script should allow a user to add a task to a list & view all tasks in the list. Store the tasks in a simple text file called
1
tasks.txt
."
See the difference? The second prompt defines the technology (Python), the interface (command-line), the exact features (add, view), & the storage mechanism (a simple text file). You've left very little room for the AI to go off on a tangent about databases or web frameworks.
2. Define the Output Format Explicitly
If you have a specific structure in mind, tell the AI. Don’t hope it guesses. This is especially useful for avoiding overly complex data structures or object-oriented designs when they're not needed.
Instead of: "Give me some code to manage user data."
Try: "Generate a single Python function that takes a user's name & email as strings. The function should return a dictionary with the keys 'name' & 'email'. Do not use a class."
By explicitly saying "a single Python function" & "Do not use a class," you're putting up guardrails. You're defining the shape of the solution you want, which is a powerful way to control complexity.
3. Provide an Example (Few-Shot Prompting)
This is one of the most effective ways to get what you want. You show the AI the style of code you're looking for. It's like giving a painter a reference image instead of just describing a scene.
Let's say you want a function, but you want it to be a simple, procedural style with comments.