Welcome to the AI Assisted Coding guide

Important

AI is no magic bullet. It is a tool to help you, but you still need to know what you are doing.

This is a guide on how to use AI Assisted Coding to your advantage. It will show you how to use the tools available to you to get the most out of AI.

This guide will cover different aspects of AI Assisted Coding and how to use them to your advantage. We also shortly cover how AI is working. We will look at different approaches to use AI effectively.

If you want a practical overview of assistant modes like Ask, Plan, and Agent, start with Modes, Context & Prompting.

Table of Contents for this guide

AI Assisted Coding VS. Traditional Coding

Traditional Coding

You write the code with explicit instructions. (HOW)
Every rule has to be manually defined.
It’s deterministic and predictable.
Full control over every step and aspect of the code.

AI Assisted Coding

You write the goal of the code. (WHAT)
AI will generate the code to achieve the goal.
Learns from patterns of millions of codebases to generate the best code.
Focus on design (WHAT) and not on the syntax (HOW).

Note

Paradigm shift from “How do I implement this?” to “What do I want to achieve?”

Vibe Coding (aka “generating AI Slop”)

You don’t know how to code
You have a vague idea of what you want to achieve
You don’t fully understand the code written by the AI
You implement features without understanding the impact or risks
You don’t know how to debug the code

Example comparison

Traditional Coding:

# Write a function to calculate the factorial of a number
def factorial(n):
    if n == 0:
        return 1
    return n * factorial(n-1)

AI Assisted Coding:

Prompt: "Write a function to calculate the factorial of a number"

Note

Role switch from “Developer” to “Software Architect”

70% system design & architecture
20% code review & quality assurance
10% manual implementation & debugging

Brief history of AI Assisted Coding

1996 – 2020: Easy autocompletion (IntelliSense)
- Syntax-based, no real understanding
2019 – 2020: Early ML completion (TabNine, IntelliCode)
- First multi-line predictions using ML
2021 – 2023: Inline suggestions (GitHub Copilot)
- Context-aware generation, “AI pair programmer”
2023 – 2024: Chat + IDE integration
- Explain, refactor, generate across files
2024 – 2025: Codebase-wide understanding (Cursor, Claude Code)
- Full repo context, debugging, refactoring
2025 – 2026: Agentic coding systems
- Multi-step tasks, autonomous edits, PRs
2026+: Autonomous multi-agent systems
- AI agents collaborating on complex goals