20 Chat GPT Codes: Copy-Paste Prompts That Actually Work

If you’re like me, you want code that just works, without wrestling with syntax or playing guess‑and‑check all afternoon. These chat gpt codes are plug‑and‑play templates you can paste into ChatGPT to build functions, fix bugs, write tests, and even draft docs. Each prompt has clear placeholders, so you can swap in your language, framework, inputs, and outputs in seconds. For best results, be specific about your task, share any error messages, and tell the model exactly how to format the answer. Use these now, then iterate as needed. You’ll also find links to proven prompt guides so you can adapt any template to your stack and IDE. Let’s keep it simple and practical for beginners who want ready‑to‑use chatgpt code examples, and yes, plenty of gpt code examples you can run right away.

Copy, paste, replace placeholders, and run. That’s it.

Start here: Make your chat gpt codes work every time

Strong prompts lead to better code. Research-backed guidance like the Prompt engineering best practices for ChatGPT shows how clear goals, constraints, and format requests improve output quality. Before you paste anything, skim this quick primer on crafting effective prompts for LLMs so you can set language, dependencies, and edge cases up front. In practice, always specify your target language (Python, JavaScript, Rust), the exact function or class you need, expected inputs/outputs, and how you want the response returned (one code block, diffs, or JSON). When you provide context, like current code, error logs, and environment details, ChatGPT can reason about performance, readability, and security while following SOLID and DRY principles. Start with these four foundational templates to structure any request.

Role + Task + Questions first

Act as a {role}. Goal: {goal}. Language: {lang}. Output format: {format}. Constraints: {constraints}. Ask up to 3 clarifying questions before coding.

Canonical code-generation template

Write {lang} code for {feature}. Requirements: {bulleted_requirements}. Include error handling, edge cases, comments. Keep existing code unchanged unless required.

Output control

Return only one fenced code block labeled {lang}. No extra prose. If assumptions are needed, list them as code comments at top.

Iteration request

Propose 3 alternative approaches with pros/cons and pick the best for {constraints}. Then implement only that choice.

Code generation: chat gpt codes you can ship

When you’re starting from scratch, prompts that name the function, parameters, and return types help ChatGPT produce clean, production‑ready snippets. See patterns and examples in Generating Code, then use the prompts below to create functions, classes, CLI scripts, and algorithms with examples and Big‑O complexity. If something refuses to run, switch to a focused bug hunt, this debugging assistant prompt helps you isolate issues, reproduce them, and propose minimal fixes. Be explicit about I/O, validation, error handling, and comments so the code is readable and maintainable. These four templates cover the most common shipping scenarios across Python, JavaScript, Java, and more, and they make great chatgpt prompts for coding when you’re moving fast.

Function generator

Create a {lang} function named {name} with params {params} returning {type}. Add docstring, input validation, and a short example usage.

Class/module blueprint

Build a {lang} class {ClassName} with constructor args {args}, public methods {methods}, and private helpers as needed. Include docstrings and unit tests.

CLI script

Write a {lang} CLI that {task}. Support flags {flags}, validate inputs, handle errors gracefully, and print helpful usage.

Algorithm with complexity

Implement {algorithm} in {lang}. Explain time/space complexity briefly and include a tiny test harness.

Debugging, reviews, and tests that catch edge cases

Great debugging prompts ask for the root cause, a minimal reproduction, and a safe fix. According to OpenAI’s guidance on the best practices for prompt engineering with the OpenAI API, precise formatting and clear goals produce stronger, more actionable responses. Paste the full traceback, the exact file or function, and your expected behavior. Ask for prioritized fixes and small diffs to keep changes easy to review. When reviewing code, request checks for potential bugs, performance pitfalls, and security risks. For tests, specify your framework and include normal, edge, and invalid inputs. Use these four prompts to stabilize your repo fast.

Error diagnosis + fix

Find root cause and fix. Show patched code only. Error: {traceback}
Code:
{lang}
{code}

Code review with priorities

```{lang}
Review this {lang} code for bugs, edge cases, performance, security, and readability. Return prioritized issues with fixes:{code}

### Unit tests

```{lang}
Generate {framework} tests for {function}. Cover happy path, edge cases, and invalid inputs. Include setup/teardown and mocks if needed.

Unexpected behavior

```{lang}
The function should {expected} but does {actual}. Provide a minimal diff to fix and one-sentence rationale.

## Refactor, optimize, and translate code safely

When refactoring or optimizing, request diffs or concise patches to avoid large, risky rewrites. The OpenAI Cookbook’s [gpt-5 prompting guide](https://cookbook.openai.com/examples/gpt-5/gpt-5_prompting_guide) explains how explicit instructions improve instruction adherence and formatting, use that to keep your changes scoped and reviewable. Ask for SOLID and DRY improvements, naming fixes, and reduced complexity. For performance, specify whether you care about speed, memory, or both, and ask for expected complexity changes and trade‑offs. When translating between languages, call out idioms and library equivalents. For data tasks, give schema details so SQL or regex is accurate. These four prompts keep your codebase clean, fast, and idiomatic.

### Refactor for clarity

Refactor for readability and SOLID/DRY without changing behavior. Return a minimal diff. Input:
{lang}
{code}

### Performance optimization

```{lang}
Optimize for {speed|memory}. Explain trade-offs and expected complexity change. Apply updates to: {code}

Language translation

```{sourcelang}
Translate from {sourcelang} to idiomatic {target_lang}. Note pitfalls and library equivalents. Code: {code}

###  SQL generator

Write a SQL query for {goal} given schema {schema}. Include an index suggestion and a sample result set.

## Docs, workflows, and non‑code deliverables that matter

Beyond code, you’ll ship docs, blog posts, resumes, and Git workflows. A hands‑on course like Pluralsight’s [Prompt Engineering Best Practices](https://www.pluralsight.com/courses/openai-chatgpt-prompt-engineering-best-practices) can sharpen your templating skills so every deliverable is consistent. For content work, try this [SEO blog post generator prompt](https://coding180.com/prompts/seo-blog-post-generator) to turn ideas into optimized first drafts. For job‑search deliverables, this [AI-powered resume prompt](https://coding180.com/prompts/ai-powered-resume-prompt) creates targeted, ATS‑friendly resumes fast. After you get a first pass from any template, adapt tone, depth, and structure using the [comprehensive prompt optimization guide](https://coding180.com/prompts/comprehensive-prompt-optimization). Use the four prompts below to standardize docs and workflows quickly across your repos and projects, handy chatgpt code examples aren’t just for code.

### Auto-docs for functions

Create clear docstrings and usage examples for every public function in this file. Note assumptions and side effects:
{lang}
{code}

### Git command sequence

Provide Git commands to {task}. Show step-by-step sequence with brief safety notes and how to undo each step.

### SEO blog draft

Draft an SEO blog post about {topic} for {audience}. Include outline, intro, H2s, FAQs, and meta title/description.

### ATS resume tailoring

Rewrite my resume for {role} using this job description: {JD}. Output ATS-friendly bullets with quantified impact and a tailored summary.
```