Cursor AI: The AI Code Editor Explained

What is Cursor AI?

Cursor is an AI-powered code editor made by Anysphere. It looks and feels like Visual Studio Code — it started as a fork of it — but rebuilds the editing experience around AI assistance. Instead of bolting a chatbot onto a sidebar, Cursor wires AI into the places you actually work: the file you are typing in, the terminal, and the project as a whole.

That distinction matters. Generic AI chat tools answer questions about code you paste into them. Cursor reads your project, understands how the pieces relate, and makes changes directly in your files with your approval.

The three core features

Tab — predictive autocomplete

Cursor's Tab feature predicts your next edit, not just the next token. It suggests whole diffs: rename a variable and it offers to update every usage; change a function signature and it proposes the matching edits at the call sites. You accept suggestions with the Tab key, which is where the feature gets its name.

Chat — ask your codebase questions

The built-in chat can see your open file, highlighted selection, or the entire indexed project. Typical uses: "where is the auth middleware applied?", "why would this query return duplicates?", or "write a test for this function." Because the chat has real project context, its answers reference your actual files instead of inventing generic examples.

Agent mode — multi-file changes from a prompt

Agent mode takes a task in plain English — "add rate limiting to all public API routes" — then plans the change, edits the relevant files, runs commands where needed, and shows you the diff to review. You stay in control: nothing lands without your approval, and you can stop or redirect it mid-task.

How Cursor differs from a plain editor with a plugin

Editor plugins like GitHub Copilot add AI suggestions to an existing editor. Cursor's bet is that deeper integration wins: codebase indexing, multi-file awareness, and an agent that can execute a plan. In practice the difference shows up on tasks that span more than one file — refactors, feature scaffolding, or tracing a bug across layers.

Because Cursor is VS Code-compatible, switching costs are low: you can import your extensions and settings and try it on a real project rather than a toy example.

Getting started

1. Download Cursor from the official site (macOS, Windows, and Linux are supported) and sign in.
2. Import your VS Code settings and extensions when prompted.
3. Open a project and let Cursor index it — this powers the context-aware features.
4. Start small: use Tab completions for a day, then try Chat on a file you know well, then hand agent mode a scoped task.

Cursor offers a free tier with limited AI usage, and paid plans that unlock more usage of the strongest models. Pricing tiers change over time, so check the official pricing page for current details.

Tips for getting good results

• Scope your prompts. "Fix the bug" invites guesswork; "the date filter returns yesterday's rows in UTC+ timezones — fix the boundary logic in reports.ts" gets precise edits.
• Review diffs like a code review. AI edits are drafts. The diff view exists so you can reject the parts that are wrong.
• Keep files reasonably small. Indexing works better when responsibilities are separated — which is also just good engineering.
• Use it for tests and documentation. These are high-value, low-risk tasks where AI assistance shines.

Privacy modes and data handling

Cursor offers two distinct modes that affect how your code is processed. In the default mode, code snippets are sent to Cursor's servers (and underlying model providers) to generate completions and answers. In Privacy Mode, Cursor commits to not storing your code on its servers after a request is fulfilled. Understanding which mode your workspace is running in matters for proprietary codebases, regulated industries, or any project with contractual restrictions on third-party data sharing.

To enable Privacy Mode, go to Cursor Settings → General → Privacy Mode and toggle it on. The setting applies per-installation, so teams working in shared environments should align on this before anyone starts coding.

A few practical considerations worth knowing:

• Privacy Mode does not change which AI models are available; it changes the data retention policy on Cursor's side, not the routing of requests to the underlying model provider.
• If your organization requires that code never leave your own infrastructure, Cursor alone does not satisfy that requirement — you would need to evaluate whether connecting to external model APIs is acceptable under your policy at all.
• The codebase index is stored locally. Cursor does not continuously upload your entire repository; it builds an embedding index on your machine and uses it to select relevant context snippets to send with each request.

Managing context: what Cursor actually sends to the model

One of the most common sources of poor results is assuming Cursor always has full awareness of your project. In practice, every request has a context window limit, and Cursor has to decide what to include. Knowing how that selection works helps you steer it.

How context is assembled

For Chat and agent mode, Cursor pulls from several sources in rough priority order:

1. The currently open file — always included, usually in full unless it is very large.
2. Explicitly mentioned files — anything you drag into the chat panel or reference with @filename is included directly.
3. Codebase search results — Cursor uses its local index to retrieve snippets it judges relevant to your query. You do not control which snippets are selected, only whether codebase search is enabled for that chat.
4. Docs and web — if you use @docs or enable web search, external content is appended, consuming additional context budget.

Where this causes problems

• If a key type or interface lives in a file that is not open and does not surface in the codebase search, Cursor may infer an incorrect signature and write code that does not compile.
• Very large files get truncated. If your main configuration object or a long enum is near the bottom of a large file, it may be cut off. Splitting large files is a practical fix, not just a style preference.
• Agent mode keeps a running context of the files it has edited in the current session. If you start a new session and ask it to continue work, it loses that history and may make inconsistent decisions. Keeping agent tasks scoped to a single session reduces drift.

Using .cursorrules to provide persistent context

A .cursorrules file placed in the root of your project is prepended to every request. This is the right place to put constraints that should always apply: preferred libraries, naming conventions, things Cursor should never do (such as introducing a specific dependency you have banned), or a short description of the architecture. Keep it focused — a long, vague rules file adds noise to every request rather than reducing it.

Model selection and when to switch

Cursor lets you choose which underlying model handles each request. The choice is not just about capability — it involves speed, cost against your monthly usage, and the nature of the task.

As a rough guide for how to pick:

• Frontier models (e.g., the most capable option available in your plan) are worth using for agent tasks that span multiple files, debugging a subtle logical error, or generating a first draft of complex logic. They are slower and count against premium request limits faster.
• Faster or smaller models suit quick questions, explaining a block of code you are reading, and inline completions where latency matters more than depth.
• Switching mid-task is valid. Start with a capable model to plan or scaffold, then switch to a faster model to ask clarifying questions about the output without burning through your quota.

One edge case: models differ in how well they follow the constraints in your .cursorrules file. If you find that a smaller model consistently ignores a rule that a larger model respects, that is a signal to treat that task class as requiring the more capable model, not a reason to remove the rule.

Frequently Asked Questions

Is Cursor AI free?

Cursor has a free tier with limited AI usage. Paid plans add more usage and access to stronger models. Exact limits and prices change, so consult the official pricing page.

Is Cursor the same as VS Code?

Cursor began as a fork of Visual Studio Code, so the interface, extensions, and keybindings are compatible — but the AI features (Tab, Chat, agent mode) are Cursor's own.

Does Cursor send my code to the cloud?

AI features work by sending relevant code context to model providers. Cursor offers a privacy mode intended to prevent code from being stored or used for training; teams with strict requirements should review the current privacy documentation.

Can Cursor write an entire feature by itself?

Agent mode can scaffold and implement multi-file changes, but results are best when you give it a clear, scoped task and review the diff. Treat it as a fast junior collaborator, not an autopilot.

Which AI models does Cursor use?

Cursor routes requests to leading frontier models and lets you choose between them for chat and agent tasks. The available model list evolves as providers release new versions.