16 Examples of Natural Language in Coding 2025

natural language programming guide

Natural language in coding lets developers write and generate code using human like language. With AI and automation, coding is now more efficient, accurate and accessible, enabling seamless translation, debugging and optimization from simple scripts to complex software.

In this guide, know 16 examples of natural language in coding 2025, exploring how AI driven advancements are making programming more intuitive and reducing the need for complex syntax.

What is Natural Language in Coding?

Natural language in coding lets developers write and generate code using human like language. With AI and automation, coding becomes more efficient, accurate and accessible, streamlining everything from simple scripts to complex software.

Benefits of Natural Language in Coding

Natural language based coding is transforming the software industry by:

• Makes coding easier for beginners.
• Increases productivity for experienced developers.
• Reduces errors and security risks with AI powered debugging.
• Speeds up development with automated testing and improvements.
• Connects human instructions with machine execution.

Examples of Natural Language in Coding 2025

Example 1: Literate Programming: Writing Code Like a Story

Literate programming is a concept introduced by Donald Knuth in 1984 where a program is written as an explanatory essay that contains both natural language descriptions and code snippets.

Unlike traditional programming where code is written for the compiler first, literate programming focuses on making code readable for humans.

➣ How It Works

Literate programming involves two main processes:

1. Weaving: Generates detailed documentation from the source code, explaining logic and structure.
2. Tangling: Extracts and compiles machine executable code from the document.

➣ Example of Literate Programming (C)

This program calculates the factorial of a given number 'n'.  
The factorial of a non-negative integer 'n' is the product of all positive integers less than or equal to 'n'.

<<*>>=
<<Include necessary headers>>
<<Define the factorial function>>
<<Implement the main function>>
@

<<Include necessary headers>>=
#include <stdio.h>
@

<<Define the factorial function>>=
int factorial(int n) {
    if (n <= 1) return 1;
    else return n * factorial(n - 1);
}
@

<<Implement the main function>>=
int main() {
    int number;
    printf("Enter a positive integer: ");
    scanf("%d", &number);
    printf("Factorial of %d is %d\n", number, factorial(number));
    return 0;
}
@

➣ Key Differences from Traditional Coding

➣ Best Use Cases

• Teaching programming (as it explains the thought process)
• Complex algorithms that require deep understanding
• Collaborative projects where documentation is essential

Example 2: Natural Language Programming (NLP) - Writing Code Like English Sentences

Natural Language Programming (NLP) allows developers to write code in plain English like syntax. Instead of using complex symbols and strict formatting, users describe logic in a way that mirrors human communication.

➣ Example of NLP Based Coding

If the user's input is greater than 10, display "High value". Otherwise, display "Low value".

➣ Equivalent Code in Python

num = int(input("Enter a number: "))

if num > 10:
    print("High value")
else:
    print("Low value")

➣ Key Differences from Traditional Coding:

➣ Best Use Cases

• Beginner friendly coding platforms (e.g., Scratch, MIT App Inventor)
• AI generated code from user instructions
• Voice based programming systems

Example 3: AI Assisted Code Generation - Let AI Write Code for You

AI assisted code generation allows developers to describe what they want in natural language and get ready made code from AI. Tools like GitHub Copilot and OpenAI Codex can convert text based descriptions into executable code.

➣ Example of AI Assisted Code Generation

User Input:
“Create a function that returns the sum of two numbers.”

➣ AI Generated Python Code:

def add_numbers(a, b):
    return a + b

➣ Key Differences from NLP Based Coding

➣ Best Use Cases

• Fast prototyping (developers save time)
• Beginner friendly learning (AI suggests best practices)
• Automating repetitive tasks

Example 4: Programming Languages with Natural Language Like Syntax

Some programming languages are specifically designed to resemble natural language. These languages reduce syntax complexity while maintaining programming logic.

➣ Examples

COBOL (Common Business Oriented Language): Designed for business applications, COBOL reads almost like English.
COBOL Code:
ADD HoursWorked TO RegularHours GIVING TotalHours.

➣ AppleScript (Used in macOS Automation)

Allows users to automate tasks using a conversational style.

➣ AppleScript Code

tell application "Finder"
    empty the trash
end tell

Inform 7 (For Interactive Fiction Games: Used to create interactive fiction stories in English like syntax.
Inform 7 Code:
The Kitchen is a room. The apple is in the Kitchen.

➣ Key Differences from Traditional Coding

➣ Best Use Cases

• Business applications (COBOL)
• Mac automation (AppleScript)
• Game development (Inform 7)

Also Read: Dynamic Programming Examples

Example 5: Natural Language Guided Programming

Natural Language Guided Programming uses AI and NLP tools to assist developers while coding by providing real time suggestions, error explanations and improvements in plain language.

Unlike AI assisted coding (which generates full code from descriptions), this method actively supports the developer while they write code.

➣ How It Works

• As a developer types, an AI assistant suggests syntax corrections, optimizations and autocomplete options.
• If an error occurs, the AI explains the issue in human readable language.
• The tool can also suggest best practices for clean, efficient code.

➣ Example — AI Explains an Error in Python

— — Developer’s Code (with an error):

print("The sum is: " + 10 + 5)

— — Traditional Error Message:

TypeError: can only concatenate str (not "int") to str

Natural Language Explanation (AI Assistant):
“You are trying to add numbers to a string. Convert the numbers to strings using str() before concatenation.”

def sort_list(numbers):
    return sorted(numbers)

➣ Suggested Fix

print("The sum is: " + str(10 + 5))

➣ Key Differences from AI-Assisted Code Generation

➣ Best Use Cases

• Debugging assistants like ChatGPT, Copilot and others
• Code review tools that suggest improvements
• Real time error explanations for beginners

Example 6: Voice Based Coding - Programming with Speech Commands

Voice based coding allows developers to write and edit code using voice commands, making programming more accessible. This method is particularly useful for:

• Developers with disabilities
• Hands free coding (e.g., while driving or multitasking)
• Speeding up repetitive coding tasks

➣ How It Works

• The user speaks a command (e.g., “Create a function that multiplies two numbers”).
• The voice assistant translates it into code and displays it on the screen.
• Users can edit, navigate and run the code using only voice commands.

➣ Example — Writing Code with Voice Commands

User says:
"Create a function called multiply that takes two numbers and returns the product."

— — Voice Assistant Generates:

def multiply(a, b):
    return a * b

➣ Popular Tools for Voice Based Coding

• Talon Voice: Full voice control over coding environments.
• VoiceCode: Specialized for programming languages.
• Windows Speech Recognition and Mac Voice Control: Basic coding support.

➣ Key Differences from Natural Language Programming

➣ Best Use Cases

• Accessibility friendly coding for disabled programmers
• Speeding up repetitive tasks like renaming variables
• AI powered hands free development

Example 7: Chatbot Based Code Assistance

Chatbot based code assistants, there are many different types of chatbots like ChatGPT, Copilot, Replit AI and more help developers write, debug and understand code through conversations.

Users can ask questions in natural language and the AI responds with explanations, fixes and even full code snippets.

➣ Example - Fixing a Bug Using a Chatbot

User: "Why is my Python loop running infinitely?"
Code:

x = 5
while x > 0:
    print(x)

Chatbot Response:
"Your loop never reduces the value of 'x'. Add 'x -= 1' inside the loop to ensure it terminates."
Fixed Code:

x = 5
while x > 0:
    print(x)
    x -= 1

➣ Key Differences from AI-Assisted Code Generation

➣ Best Use Cases

• Beginners learning coding concepts
• Debugging and fixing errors
• Understanding complex code snippets

Example 8: Low-Code and No-Code Platforms

Low-code and no-code platforms allow users to build applications without writing traditional code. These platforms use drag and drop interfaces and natural language instructions to automate coding.

➣ Example - Creating an App Without Coding

Using a tool like Bubble.io, a user can describe their app:
"When a user clicks the submit button, store the input in the database and display a success message."
The platform then automates the underlying code, creating a functional app without manual programming.

➣ Key Differences from NLP-Based Coding

➣ Best Use Cases

• Business users who need custom apps
• Automating workflows without coding
• Building prototypes quickly

Also Read: Generative AI Project with Source Code

Example 9: Human Friendly Error Messages

Instead of cryptic error messages, human friendly errors explain mistakes in plain language to help developers understand and fix them quickly.

➣ Example - Python Error Messages

Traditional Error:
-- -- TypeError: unsupported operand type(s) for +: 'int' and 'str'

Human Friendly Explanation: "You are trying to add a number and a string. Convert one to match the other."
Languages like Python, Elm and Rust provide detailed, beginner friendly error messages to improve debugging.

➣ Best Use Cases

• Teaching beginners
• Reducing debugging frustration

Example 10: Future of Natural Language in Coding

The future of coding is moving towards AI driven, human friendly programming where developers can describe what they want in natural language and AI generates the necessary code or automates the task.

➣ Key Trends and Advancements

1. Conversational Programming — Developers will write entire applications using voice or text based conversations with AI.
2. Self Optimizing Code — AI will suggest performance improvements in real time.
3. Code Explanation and Documentation Generation — AI will automatically document complex codebases, making them easier to understand.

➣ Example - Future Conversational Coding

Developer Input (Natural Language): "Create a web page with a form that collects name and email, then stores it in a database."
AI Generated Code (HTML + JavaScript + Backend):

<form id="userForm">
  <input type="text" id="name" placeholder="Enter your name">
  <input type="email" id="email" placeholder="Enter your email">
  <button type="submit">Submit</button>
</form>
<script>
  document.getElementById('userForm').addEventListener('submit', function(event) {
    event.preventDefault();
    let name = document.getElementById('name').value;
    let email = document.getElementById('email').value;
    fetch('/save', { method: 'POST', body: JSON.stringify({ name, email }) });
  });
</script>

➣ Key Differences from Traditional Coding

➣ Best Use Cases

• Rapid prototyping
• AI powered development environments
• Code optimization suggestions

Example 11: AI Powered Debugging and Error Fixing

AI powered debugging tools analyze code in real time, detecting bugs, inefficiencies and security vulnerabilities while providing human friendly explanations and fixes.

➣ How It Works

• AI scans the code line by line for potential issues.
• The tool suggests fixes in natural language, making debugging easier.
• Developers can approve, modify or reject the suggestions.

➣ Example — AI Debugging in Python

— — Buggy Code:

def divide_numbers(a, b):
    return a / b  # No handling for division by zero

AI Explanation: "This function may cause a division by zero error. Consider adding a check."

— — AI Suggested Fix:

def divide_numbers(a, b):
    if b == 0:
        return "Error: Cannot divide by zero."
    return a / b

➣ Key Differences from Manual Documentation

➣ Best Use Cases

• Faster debugging cycles
• Beginner friendly coding environments
• Preventing security vulnerabilities

Example 12: AI Powered Code Documentation

AI tools automatically generate documentation for code, explaining functions, variables and logic in plain English.

➣ How It Works

1. AI analyzes code structure and understands function purposes.
2. It generates descriptive comments and API documentation.
3. Developers can modify the AI generated docs for better clarity.

➣ Example — AI Generated Python Documentation

— — Original Code:

def calculate_area(radius):
    return 3.14 * radius * radius

— — AI Generated Documentation:

def calculate_area(radius):
    """
    Calculates the area of a circle given its radius.
    
    Args:
        radius (float): The radius of the circle.
    
    Returns:
        float: The area of the circle.
    """
    return 3.14 * radius * radius

➣ Key Differences from Manual Refactoring

➣ Best Use Cases

• Large scale software projects
• Maintaining clear documentation for teams
• Open source contributions

Also Read: Git vs GitHub Difference

Example 13: Code Refactoring with Natural Language

AI powered refactoring tools optimize code for readability, performance and maintainability based on natural language descriptions.

➣ How It Works

• Developers describe how they want to improve the code (e.g., “Make it more efficient”).
• AI analyzes the code and refactors it accordingly.

➣ Example — AI Code Refactoring in Python

— — Before Optimization:

numbers = [1, 2, 3, 4, 5]
squares = []
for num in numbers:
    squares.append(num * num)

User Request: "Optimize this code."

— — AI Suggested Refactored Code:

squares = [num ** 2 for num in [1, 2, 3, 4, 5]]

➣ Key Differences from Manual Translation

➣ Best Use Cases

• Large codebases needing cleanup
• Optimizing performance automatically
• Reducing redundancy in code

Example 14: AI Powered Code Translation

AI powered code translation enables developers to convert code from one programming language to another automatically while preserving functionality, logic and best practices.

➣ This is particularly useful when

• Migrating a project to a different programming language.
• Understanding legacy code written in an unfamiliar language.
• Translating code between frameworks or platforms.

➣ How It Works

1. The AI analyzes the syntax, structure and logic of the original code.
2. It translates the code into the target language, ensuring it follows the correct syntax and conventions.
3. AI may also optimize the translated code for efficiency.

➣ Example — Python to Java Conversion

— — Original Python Code:

def greet(name):
    return f"Hello, {name}!"

— — AI Converted Java Code:

public class Main {
    public static String greet(String name) {
        return "Hello, " + name + "!";
    }
}

➣ Key Differences from Manual Code Translation:

➣ Best Use Cases

• Migrating applications from Python to Java or vice versa.
• Translating algorithms into different programming languages for compatibility.
• Understanding unfamiliar codebases in a new language.

➣ Popular Tools

• OpenAI Codex (GPT based translation)
• CodeConvert.ai
• AWS CodeWhisperer

Example 15: Automated Code Reviews with AI

AI powered code review tools analyze code for quality, security, efficiency and adherence to best practices. These tools provide human readable feedback to help developers improve their code.

➣ How It Works

1. The developer submits code for review.
2. AI scans the code for bugs, vulnerabilities, inefficiencies and style violations.
3. The tool provides detailed feedback, suggesting fixes and improvements.

➣ Example — AI Code Review Comment

— — Developer’s Code:

def get_data():
    file = open("data.txt", "r")
    return file.read()

AI Review Comment: "Consider using a with statement to handle file operations safely. This prevents resource leaks."

— — Suggested Fix:

def get_data():
    with open("data.txt", "r") as file:
        return file.read()

➣ Key Differences from Human Code Reviews

➣ Best Use Cases

• Ensuring consistent coding standards across teams.
• Detecting security vulnerabilities in real time.
• Optimizing code for better performance and maintainability.

➣ Popular Tools

• SonarQube
• DeepCode
• Codiga

Example 16: AI Assisted Automated Testing

AI powered testing tools automatically generate, execute and analyze test cases, reducing the effort needed for manual testing. These tools help developers find bugs, improve reliability and ensure code correctness.

➣ How It Works

1. AI analyzes the code and identifies critical paths that need testing.
2. It generates automated test cases based on code behavior.
3. AI runs the tests and provides detailed reports on failures and potential issues.

➣ Example — AI Generated Test Cases for Python Code

— — Original Code:

def add(a, b):
    return a + 

— — AI Generated Unit Test (Using PyTest):

import pytest
from my_module import add

def test_add():
    assert add(2, 3) == 5
    assert add(-1, 1) == 0
    assert add(0, 0) == 0

➣ Key Differences from Manual Testing:

➣ Best Use Cases

• Automated regression testing to catch bugs early.
• Generating test cases without manual effort.
• Reducing human errors in testing processes.

➣ Popular Tools

• Testim
• Applitools

Also Read: PHP Project Topics

Conclusion

Many top-rated and best software development company in India are using natural language in coding to improve efficiency, accessibility and client friendliness. AI tools help developers write, debug and optimize code in plain English, streamlining the entire development process.

As AI advances, examples of natural language in coding 2025, like AI driven code generation and automated debuggin will continue to redefine the future of programming.

FAQs

Q1. How Does AI Assisted Code Generation Work?
AI assisted code generation uses machine learning models trained on vast code datasets to understand prompts written in natural language. Tools like OpenAI Codex generate, optimize and debug code automatically based on user descriptions.

Q2. Are Low-Code and No-Code Platforms the Future of Programming?
Low-code and no-code platforms simplify development by allowing users to build applications using drag and drop interfaces and minimal coding.

While they streamline software creation, traditional coding remains essential for complex and scalable solutions.

Q3. Can Voice-based Coding Replace Traditional Programming?
Voice based coding enables hands free development by allowing programmers to dictate commands, making coding more accessible. However, due to precision requirements, it is more likely to complement rather than replace traditional programming.

Q4. How is Natural Language Coding Changing Software Development?
Natural language coding is making software development more intuitive by allowing developers to write, debug and optimize code using plain English.

Natural Language Coding Examples include AI powered code generation, automated debugging and voice based coding which streamline programming and make it accessible to non experts.

Q5. How Does SQL Use Natural Language Principles?
SQL follows natural language principles by using readable commands like SELECT, WHERE and ORDER BY, making database queries intuitive. This structure allows users to retrieve and manipulate data with statements resembling everyday language.