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Cursor

As explained in Coding with AI

Cursor is an AI-assisted code editor that supports software development through natural-language interaction and code generation.

Cursor is an AI-centered code editor for vibe coding: developers describe a goal in natural language while an agent explores a project, plans work, edits files, runs terminal commands and tests, then reports what it did. Its interface resembles Visual Studio Code, but its workflow is built around LLM interaction and supervised autonomous tasks. section 10.2.1 section 10.5

Project-wide context lets Cursor use a codebase’s structure, dependencies, patterns, and documentation for multifile generation, refactoring, linting, and context-aware questions. Project rules such as .cursorrules can place team conventions in the agent’s instructions. This can accelerate small applications, but larger ones may still require manual fixes. section 10.2.2

Choose a mode based on the task:

  • Agent plans, searches, builds, and carries out requests such as creating a database interface. section 10.5.3
  • Ask answers questions about APIs or possible improvements to code. section 10.5.3
  • Manual sends general-purpose prompts directly to the LLM without tool use. section 10.5.3

Iterate on a small game

Start with a detailed specification for a one-file Python Frogger-style game: runtime, library version, window, movement, scoring, code organization, and quality constraints. After testing, describe an observed problem plainly: logs overlap. Cursor can revise lane speeds, spacing, respawning, and log counts; review its explanation and the proposed diff before accepting individual changes or the whole file. section 10.4 section 10.8

Figure 10.10 There is a diff interface that comes up in Cursor. You can accept individual changes or accept the entire file if you like.
A screenshot of a computer program

AI-generated content may be incorrect.

The diff view makes generated edits reviewable: accept individual changes or accept an entire file after inspecting the proposal. section 10.8

Treat natural-language generation as a fast prototyping loop, not a replacement for judgment. Give relevant context and clear constraints to reduce rework, then retain structured practices, accountability, and thorough testing for production, security-sensitive, and enterprise systems. section 10.9 section 10.4

How Coding with AI covers it

How deep it goes
Demonstrates agent modes, project context, prompting, generated revisions, and diff review; notes manual work on larger applications.
Examples
Uses a Python Frogger-style game, then fixes overlapping logs through a plain-English request and reviewed diff.
What it assumes
Assumes basic technical skills rather than programming expertise.
What you can do after
Apply it to prototype and revise small applications through prompts while reviewing agent-generated changes.
How it connects
Connects vibe coding with AI agents, prompt engineering, context management, code generation, refactoring, and linting.

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