AI Coding Assistants

Introduction

AI coding assistants — Copilot, Cursor, Claude Code, Aider, Continue — have changed how developers work. Building one combines LLMs, code-aware retrieval, and IDE integration.

Beginner analogy: Like pair programming with someone who has read every open-source repo.

Understanding the topic

Core concepts to understand:

• Index the codebase into a vector DB.
• Retrieve relevant files for each prompt (RAG).
• Use long-context models (Claude, Gemini) for big diffs.
• Stream edits back as patches the user can accept/reject.

Syntax reference

Visual workflow / architecture:

IDE event (cursor, hover, prompt)
       │
       ▼
   Retrieve relevant code (RAG)
       │
       ▼
   LLM with prompt + context
       │
       ▼
   Stream code edits
       │
       ▼
   Diff UI → accept / reject

Real-world use

GitHub Copilot (OpenAI), Cursor (Claude/GPT-4), Replit Ghostwriter, Tabnine, Continue. Cursor reached $100M+ ARR with a small team.

Best practices

• Index incrementally — don't re-embed the whole repo on every change.
• Always show a diff before applying changes.
• Allow easy undo / rejection.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. How does Copilot decide what code to suggest?
• Q2. What role does RAG play in coding assistants?
• Q3. Why use long-context models for code?