Cursor AI for Production Apps: Beyond Autocomplete to Real Workflows

Composer, Chat, and Inline Edit: Knowing Which to Use

Most developers who pick up Cursor AI get stuck using it as a smarter autocomplete — which is like using a dishwasher to rinse a single cup. The tool has three distinct interaction modes, and choosing the right one per task is where the productivity gains actually come from.

Inline Edit (Cmd+K) is the right choice for fast, localised changes: renaming a function, adding error handling to an existing block, converting a class component to a hook. It operates on selected code and returns a diff you can accept or reject. Keep it for changes where the scope is entirely visible on screen.

Chat is Cursor's conversational interface with @-mention support. It's best when you need to understand the codebase before changing it — ask it to explain a confusing utility function, trace where a particular state variable is consumed, or diagnose why a type error is appearing. You can reference specific files with @filename, and the response includes code snippets you can apply. Think of Chat as the reading and reasoning mode, not the writing mode.

Composer (Cmd+Shift+I) is where multi-file edits happen. When you need to add a new feature that touches a database schema, a server action, a UI component, and a type definition simultaneously, Composer is the appropriate tool. It generates a diff across all affected files and lets you review changes file by file before accepting. For any task that spans more than one file, bypass Chat and Inline Edit entirely and go straight to Composer.

Making Cursor Understand Your Entire Project

The single biggest quality jump with Cursor AI comes from getting codebase indexing working properly. By default, Cursor indexes your project files so the @codebase context tag can pull relevant snippets into any prompt. But the index is only as good as what you let it scan.

The first thing to configure is your .cursorignore file. Add build directories, generated files, large JSON fixtures, and vendor folders. A bloated index means Cursor retrieves irrelevant matches and your prompts get polluted with noise. A lean index — just your actual source code — produces dramatically better @codebase results.

Once the index is clean, use @codebase in Composer prompts for any feature that needs awareness of existing patterns. A prompt like "Add a Razorpay webhook handler following the same pattern as the existing Stripe webhook handler — @codebase" will produce code that mirrors your existing error handling, logging structure, and response format rather than a generic implementation. Without @codebase, you get a generic Razorpay handler. With it, you get one that fits your codebase.

For Kerala-based projects that mix Next.js with Supabase and Razorpay, the index is especially valuable when dealing with row-level security policies. Asking Cursor to generate a new RLS policy for a multi-tenant table works far better when it can reference your existing policies as examples.

.cursorrules: Project-Specific Conventions That Stick

The .cursorrules file lives in your project root and acts as a persistent system prompt for every Cursor interaction in that project. A well-structured .cursorrules file is worth writing carefully — it eliminates the repetitive overhead of explaining your conventions in every single prompt.

A practical .cursorrules for a Next.js + Supabase project in India might include: your preferred query wrapper instead of raw Supabase client calls, the fact that all monetary values are stored in paise (not rupees), that GST calculations always go through a specific utility function, the file naming convention (kebab-case for pages, PascalCase for components), and that Malayalam text in the UI uses the Noto Sans Malayalam font class rather than system fonts.

You can also encode linting preferences — "never use any as a TypeScript type, always narrow explicitly" or "use zod for all API input validation, never manual type assertions." These rules are applied passively; you don't have to invoke them. When Cursor generates code, it references these rules the same way it references your prompt.

One pattern that works well for Indian development teams: add a section to .cursorrules specifying how to handle Unicode in Malayalam string operations. Malayalam Unicode has some non-obvious edge cases around Chillu characters and conjuncts that generic AI code gets wrong without explicit guidance.

Agent Mode: What It Handles Well and Where It Fails

Agent mode gives Cursor the ability to run terminal commands, read error output, edit files, and iterate until the task is done — without manual intervention between steps. It sounds transformative, and for the right tasks, it genuinely is.

Where agent mode performs reliably: tasks with a clear terminal state that touch under five files. "Add a full CRUD API for the appointments table following the pattern of the invoices API" is an excellent agent mode task. It's well-defined, the success state is observable, and the file scope is bounded. Cursor will write the route handlers, add the Zod schemas, wire up the Supabase queries, and usually get it right on the first attempt.

Where it goes off-rails: large-scope refactors, tasks with ambiguous success criteria, and anything that requires understanding business context rather than code patterns. "Refactor the authentication flow to support multi-tenancy" will produce something — but it will make opinionated architectural decisions you didn't sanction, contradict changes from earlier in the same session, and leave you with a partially-refactored codebase that's harder to untangle than the original. For refactors of this size, use Cursor for research and planning, then execute each sub-task manually.

A practical safeguard: always work in a git branch when using agent mode. The ability to git diff the output before merging, and to git checkout . if the agent went somewhere unexpected, makes agent mode much less risky. Treating every agent mode session as a draft review, not a final commit, is the right mental model.

Using Cursor for Debugging with Terminal Output

One of the more underused Cursor workflows is paste-and-diagnose debugging. When you hit an error — a Next.js build failure, a Supabase RLS violation in the logs, a TypeScript compilation error — paste the full error output into Cursor Chat along with a reference to the relevant file. The model often identifies the root cause faster than searching Stack Overflow, especially for errors that involve interacting systems (like a Next.js server action failing because of a missing Supabase service role key in a specific environment).

The key is to paste the complete error output, not a trimmed version. Cursor needs the stack trace, the environment context, and any preceding log lines to make an accurate diagnosis. A truncated error message produces a generic guess; a full error output produces a specific answer.

For GST invoice generation code — a common pain point for Indian SaaS products — Cursor is particularly useful for debugging number formatting edge cases. GST percentage calculations that look correct in development but fail on amounts like ₹99.99 (due to floating point) are exactly the kind of subtle bug where pasting the failing test case into Chat and asking for a root cause analysis saves significant time.

Cursor Pro vs GitHub Copilot: An Honest Comparison

As of early 2026, Cursor Pro costs approximately ₹1,700 per month and GitHub Copilot Individual runs approximately ₹1,000 per month for Indian accounts (exact pricing varies with exchange rates and billing region). The question of which to use isn't purely about price — it's about which workflow fits your actual development pattern.

GitHub Copilot's strengths are its deep VS Code integration and its Tab completion, which is marginally better trained on general-purpose patterns. If you spend most of your time in a single file, making incremental edits, Copilot's friction is lower. It also has the advantage of working inside VS Code without a context switch.

Cursor's advantage is Composer and @codebase. If your work routinely involves features that touch multiple files — which it does for most product developers — the cross-file awareness pays for the ₹700/month premium. Some developers run both: Copilot for Tab autocomplete in the default editor and Cursor for planning and multi-file feature work. This isn't as redundant as it sounds, since the two tools serve different interaction patterns.

One thing neither tool handles well: complex business logic rewrites. When you need to change how your application models a domain concept — not just how it codes one — human architectural judgment is still required. Generate code with Cursor, but review it with the same care you'd apply to a junior developer's pull request. The model doesn't understand your business constraints; it only understands your code patterns.

Honest Limitations in 2026

Long file context remains a genuine weakness. When a single file exceeds roughly 500 lines, Cursor's inline suggestions become less accurate — the model starts losing track of what's defined at the top of the file by the time it's generating code at the bottom. The practical fix is to keep files small (which is good practice anyway) and rely on @codebase references rather than inline context for large files.

Complex logic rewrites — changing an algorithm, not just its style — are still unreliable. Cursor will produce something that looks plausible but often introduces subtle logic errors that only surface in edge cases. Always write tests before asking Cursor to rewrite non-trivial logic, so you have an automated way to verify the output.

The final, non-negotiable rule: verify generated code before shipping to production. This isn't a Cursor-specific limitation; it applies to every AI coding tool. The workflow should always be generate → read → test → ship, never generate → ship. The time saved by Cursor is real, but it's saved in the writing phase, not the review phase.