From Natural Language to Working Code

One of AI's most impressive capabilities is translating human descriptions into functional code. This lesson teaches you to describe programming tasks effectively so AI generates exactly what you need—whether you're a beginner learning to code or an experienced developer accelerating routine tasks.

The Code Generation Formula:

Effective prompts often include:

[Programming language] + [Task description] + [Input/Output specs] + [Constraints] + [Style preferences]

Basic Example:

Weak prompt: 'Write a function to sort numbers'

Strong prompt: 'Write a Python function that takes a list of integers and returns them sorted in descending order. Handle empty lists by returning an empty list. Include docstring and type hints.'

Element 1: Specify Language & Context:

Always name the language (e.g. Python, JavaScript) and any frameworks/libraries. Context ensures AI picks correct imports & idioms.

Element 2: Clear Task Description + Steps

E.g. break complex tasks into sub-steps so AI can follow logically.

Element 3: Input / Output Spec

Define data types, edge cases, formats, etc.

Element 4: Constraints & Requirements

Memory, performance, libraries, style rules.

Prompt Patterns:

Function generation, class scaffolding, API endpoint creation, test generation, etc.

Iterative Refinement:

First get basic working version, then request improvements in subsequent prompts.

Language-specific tips & common mistakes:

Don’t overload prompt, avoid vague instructions, always specify format.