Best AI Prompts for Git Commit Messages with Cursor

Quick Answer

We’ve analyzed the critical need for high-quality Git commit messages in modern development workflows. This guide provides a strategic framework for leveraging Cursor AI to generate context-aware, descriptive commits that eliminate technical debt and improve team collaboration. Our approach transforms commit messages from a chore into a strategic asset for your codebase.

Key Specifications

The Art of the Commit Message in the AI Era

Ever stared at your screen, fingers hovering over the keyboard, dreading the inevitable? You’ve just finished a complex refactor, untangling a gnarly piece of logic, and now you have to distill all that mental effort into a single, pithy line: git commit -m "fix stuff". We’ve all been there. This isn’t just a minor annoyance; it’s a productivity killer. Writing commit messages is often treated as a chore, a final bureaucratic hurdle before you can push your code. The result? A git history littered with vague, unhelpful messages like wip, updates, or debug. This creates a massive bottleneck, turning code reviews into forensic exercises and long-term maintenance into a guessing game.

This is where the game changes entirely. Cursor, an AI-powered code editor, doesn’t just complete your lines of code; it understands your entire project. It sees the relationships between files, the specific functions you’ve modified, and the overall architecture. This unique, context-aware capability makes it the perfect partner to solve the commit message dilemma for good.

In this guide, we’ll move beyond generic, one-line autocompletion. We’ll explore how to craft specific, powerful prompts that leverage Cursor’s deep understanding of your codebase. The goal is to generate granular, descriptive commits that tell a story—transforming fix bug into Refactor: auth.ts logic for token refresh. This isn’t just about cleaner logs; it’s about reclaiming your focus and making your git history a strategic asset for your entire team.

H2: Why Your Current Commit Messages Are Failing (And Why It Matters)

You’ve just spent three hours hunting down a frustrating bug. The fix itself was a single line change, but the journey to find it was a marathon of debugging. You finally commit your work with a sigh of relief: git commit -m "fix bug". You’ve done your duty, right? Not even close. You’ve just created a black hole of information that will waste future hours of your time—or your team’s.

This scenario plays out thousands of times a day, creating a silent but crippling form of technical debt. A commit message like fix bug is a dead end. It fails the most basic test of a useful commit: it provides zero context. It doesn’t answer the critical questions:

• What was the specific nature of the bug? Was it a race condition, a null pointer exception, or an off-by-one error?
• Why did the bug occur? Was it a flawed assumption in the original code, an edge case not covered by tests, or a dependency update that broke existing logic?
• How was the solution implemented? Did you add a null check, refactor a function, or change a conditional statement?

Without these answers, git blame becomes an exercise in archaeology, not engineering. A developer looking at that change six months from now (which will likely be you) has to reverse-engineer your entire thought process from the surrounding code. This is slow, frustrating, and prone to misinterpretation, often leading them to “fix” something that wasn’t actually broken.

The Team Collaboration Tax

The impact of poor commit messages extends far beyond your own future frustration; it acts as a direct tax on team productivity. Think of your commit history as the project’s primary communication tool for asynchronous collaboration.

When a new developer joins your team, their first step in understanding the codebase is often git log. A history filled with messages like wip, update, and fix stuff is like a book with half the pages missing. They can’t trace the project’s evolution, understand why certain architectural decisions were made, or learn from past mistakes. This dramatically increases their onboarding time and cognitive load.

Code reviews suffer just as much. A reviewer receiving a pull request titled Fixes issue #452 has no starting context. They are forced to manually inspect every single line of the diff to deduce the intent. Compare that to a commit that reads:

fix(auth): resolve race condition in token refresh logic

The second message immediately focuses the reviewer’s attention. They know to look for timing issues in the authentication module. The review becomes faster, more targeted, and less prone to human error. A well-crafted commit message is a gift to your future code reviewer.

The Hidden Cost of Bad Commits as Technical Debt

We talk about technical debt in terms of messy code, lack of tests, or outdated libraries. It’s time we add “poor commit hygiene” to that list. Every low-effort commit message is a small withdrawal from your project’s long-term maintainability account.

A clean, descriptive Git history is a valuable project asset. It pays dividends in several ways:

• Debugging Velocity: When a regression appears, the first thing you do is git bisect. A series of descriptive commits allows you to pinpoint the exact change that introduced the bug in minutes, not hours.
• Effective Refactoring: To refactor safely, you must understand the purpose of the code you’re touching. A commit history that explains the why behind functions and modules gives you the confidence to make changes without breaking hidden business logic.
• Knowledge Retention: Team members leave. Projects get shelved and resurrected. A detailed commit history is the institutional memory of your codebase, preserving context that would otherwise be lost.

Expert Insight: In my experience auditing dozens of enterprise codebases, I’ve found a direct correlation between commit message quality and bug density. Teams that treat their git log as a project narrative consistently produce more stable, maintainable software. They spend less time in detective mode and more time building features.

Ultimately, the friction of writing good commits manually is the root cause. It requires a context switch from the logical flow of coding to the narrative flow of writing. This is where modern AI, especially context-aware tools like Cursor, changes the equation. By automating the “writing mode,” you can eliminate the friction and consistently produce the granular, descriptive history your project deserves—like turning a vague fix bug into a precise Refactor: auth.ts logic for token refresh.

H2: The Cursor Advantage: Context-Aware Commit Generation

Have you ever stared at a git diff and struggled to summarize it into a meaningful commit message? It’s a common chore that pulls you out of your creative flow. Standard AI assistants can offer suggestions, but they’re often generic because they lack one crucial ingredient: context. They see your request in a vacuum, unaware of the specific function you just spent 20 minutes refactoring. This is where Cursor fundamentally changes the game, moving beyond simple autocomplete to become a true partner in your development workflow.

Beyond Simple Autocomplete: It’s About Project Awareness

The core difference lies in what the AI can “see.” A standard AI assistant is like a brilliant coder you’re messaging over chat. You can ask it a question, but it has no memory of the files you have open or the specific lines you just changed. It requires you to manually copy, paste, and explain the context every single time.

Cursor, on the other hand, is integrated directly into your editor. It has a live, dynamic view of your entire workspace. This means it’s not just processing your text prompt; it’s analyzing:

• The exact function you’re currently editing.
• The surrounding code and imports.
• Other files in your project that might be related.

When you ask Cursor to generate a commit message, it doesn’t just guess based on your words. It sees the specific if statement you added, the variable you renamed, and the new test case you wrote. This allows it to produce a suggestion like “Refactor: auth.ts logic for token refresh” instead of the generic “Update auth file.” This is the difference between a vague hint and a precise, actionable summary.

How Cursor “Reads” Your Code: The Senior Developer Analogy

Think of Cursor as a senior developer looking over your shoulder, actively watching you work. You don’t have to explain every single change from scratch. As you type, it’s observing the context, understanding the intent behind your modifications. When you finally ask it to summarize your work, it already knows the story.

This is powered by its context window. In simple terms, this is the AI’s working memory. For a developer, it’s like having an expert who has just reviewed your entire pull request in seconds. They can see that you modified the login.ts function and the user.test.ts file, and they can infer that you likely fixed a bug and added a test to prevent it from reoccurring. This ability to connect the dots across multiple files is what enables truly granular and intelligent commit messages. It understands the “why” because it sees the “what” and “where.”

Setting the Stage: The Power of the Git Diff

To unlock the highest level of precision, you need to give Cursor the raw data it needs: the git diff. While it can infer a lot from your open files, prompting it to analyze the git diff is like handing your senior developer a detailed code review summary before they give their final opinion. This is the single most effective way to ensure your generated messages are perfectly aligned with your actual changes.

The git diff is the definitive record of what was added, removed, and modified. By instructing Cursor to analyze this, you eliminate all ambiguity. It can see line-by-line exactly what you accomplished, allowing it to craft a message that is not just context-aware but diff-aware. This is the foundation for the advanced prompting techniques that will transform your git history from a simple log into a rich, searchable project narrative.

H2: The Core Prompting Framework for Granular Commits

Why do some developers produce commit histories that read like a clear, professional narrative, while others leave behind a cryptic trail of wip, fix, and changes? The secret isn’t a superior tool; it’s a superior framework. Before you can write a single line of code, you need a mental model for what makes a commit message truly useful. This is the foundation upon which all effective AI prompting is built.

The most common mistake is asking an AI to “write a commit message” without providing a structure. This is like asking a junior developer to “fix the bug” without any context. The result is often generic and misses the critical details. To consistently generate high-quality, granular commits, we need to give the AI a clear, repeatable formula. We’ll base our entire framework on the three essential pillars of any meaningful change: What, Why, and How.

The “What, Why, and How” Formula: A Non-Negotiable Structure

This simple formula is the bedrock of professional communication in software development. It forces clarity and provides a logical flow that anyone reading the history can follow. When crafting your prompts for Cursor, you must instruct it to analyze the code diff and extract these three specific pieces of information.

1. What: This is the high-level action or intent. It answers the question, “What did you set out to accomplish?” Examples include Refactor, Fix, Feat, Update, or Remove. This provides the immediate context for the change.
2. Why: This is the most critical, yet most often omitted, piece of information. It answers the question, “Why was this change necessary?” Was it to resolve a specific bug report (e.g., addresses issue #123), improve performance, or address a security vulnerability? The “why” provides the business or technical rationale.
3. How: This describes the technical implementation or approach. It answers the question, “How did you achieve the change?” Examples include by simplifying the token refresh logic, by adding a null check to the user controller, or by migrating to the new API endpoint.

By explicitly instructing the AI to find and combine these three elements, you move from generic messages to highly specific, actionable commit histories.

Prompt 1: The Standard Granular Commit

This is your workhorse prompt. It’s designed for daily use, providing a clean, concise format that is immediately understandable without being overly verbose. It directly applies the “What, Why, and How” framework in a compact way.

When you run a git diff in Cursor, you have the perfect context. Feed that diff to the AI with this instruction.

The Prompt: Analyze the git diff and generate a concise, professional commit message. Follow the format: "[Action]: [Specific Area/Function] - [Brief Description of Change]". Example: "Refactor: auth.ts - Simplify token refresh logic".

Why this works:

• Action (What): The [Action] placeholder forces the AI to identify the primary intent (e.g., Fix, Feat, Refactor).
• Area (How): The [Specific Area/Function] ensures the message points directly to the file or function that was modified, making it easy to locate the change later. This is the “granular” part of the promise.
• Description (Why/How): The [Brief Description of Change] is where the AI synthesizes the “why” and “how” from the diff. It’s prompted to be concise, forcing it to focus on the most important aspect of the change.

This prompt is your first line of defense against a messy history. It’s simple, effective, and builds a consistent pattern across your project.

Prompt 2: The Conventional Commits Specialist

Many professional teams adopt a formal standard like Conventional Commits. This specification provides a lightweight, structured way to write commit messages that can be automatically parsed by tools to generate changelogs, determine semantic version bumps, and communicate changes across teams.

If your team uses this standard, you can easily adapt your prompting strategy. The key is to provide the AI with the rules of the specification and a few clear examples.

The Prompt: Generate a commit message following the Conventional Commits specification. Use a type like feat, fix, refactor, or chore. Include a scope if applicable. Analyze the diff for context. Example: "fix(auth): Correctly handle expired refresh tokens".

Expert Insight: A “Golden Nugget” for Scopes

A common pitfall with Conventional Commits is creating overly generic scopes (e.g., fix(api): ...). The real power comes from specificity. When prompting Cursor, add a line like: “Be specific with the scope. Instead of ‘api’, use ‘user-api’ or ‘auth-api’ if the diff shows changes in those specific modules.” This small addition trains the AI to mirror the granularity of your codebase, making your automated changelogs infinitely more valuable.

Why this works:

• Enforces Structure: The prompt explicitly names the required components (type, scope), ensuring the output is compliant.
• Context-Aware Scoping: By telling the AI to analyze the diff for context, it can automatically determine the correct scope (e.g., seeing changes in auth.ts and setting the scope to auth).
• Maintains Consistency: By providing a clear example, you set the standard for the tone and format, which is crucial for team-wide consistency.

Using these two prompts as your foundation will fundamentally change how you interact with your git history. You’re no longer just recording changes; you’re curating a valuable, searchable project narrative.

H2: Advanced Prompting Strategies for Complex Changes

Once you’ve mastered basic commit generation, you’ll quickly encounter scenarios where a simple diff analysis isn’t enough. What happens when a change is driven by a subtle bug fix that isn’t obvious from the code? Or when you’ve refactored a feature across five different files? This is where moving from a generic AI assistant to a true coding partner becomes critical. Advanced prompting isn’t about writing longer sentences; it’s about providing strategic context that guides the AI to produce a more nuanced, valuable output.

These strategies are designed for the messy reality of development—the non-linear, multi-file, bug-fixing sprints that define most projects. By teaching Cursor how to think about the change, not just what changed, you unlock a new level of productivity and clarity in your git history.

Prompt 3: The “Explain the ‘Why’” Commit

Code tells you what happened, but commit messages should tell you why. This is the single most common failure point in team development. A junior developer sees a change like if (user && user.profile) and might not understand that it’s preventing a critical null pointer exception that was crashing the production server. The “Explain the ‘Why’” prompt forces the AI to infer the intent and reasoning behind a change, turning a cryptic diff into a clear narrative.

This is my go-to prompt for any bug fix or subtle logic adjustment. It’s the difference between a history that says Fix: user profile and one that says Fix: Prevent null pointer in profile fetch by adding optional chaining. The latter saves hours of detective work during a future refactor.

The Prompt: Based on the git diff, generate a commit message that explains the problem this change solves. Start with the issue, then describe the solution. Example: "Fix: Prevent null pointer exception in user profile fetch by adding optional chaining".

When you use this, you’re essentially asking the AI to play the role of a detective. It analyzes the code, identifies the potential failure point (e.g., accessing a property on a potentially null object), and articulates the fix in plain English. This is an invaluable practice for code reviews, as it provides immediate context to your teammates, reducing the cognitive load required to approve a pull request.

Prompt 4: The Multi-File Change Commit

The true power of a context-aware editor like Cursor shines when you’re making architectural changes that span multiple files. You might be touching the frontend component, the API route, the database schema, and the validation logic all at once. A commit message that only describes one of these files is incomplete and misleading.

The challenge here is synthesis. You don’t want a laundry list of every single file change; you need a high-level summary that captures the essence of the feature. This prompt instructs Cursor to zoom out and see the forest for the trees, focusing on the user-facing impact or the core architectural shift.

The Prompt: Summarize the changes across these multiple files into a single, coherent commit message. Focus on the overall user-facing impact or the main architectural change. Example: "feat: Implement user avatar upload pipeline from frontend to S3".

This prompt is a game-changer for feature development. It ensures your commit history is readable at a glance. Six months from now, when you’re browsing the log, feat: Implement user avatar upload pipeline is immediately understandable, whereas a list of five separate, file-specific commits would require you to open each one to piece together the story. It’s about building a history that’s useful for future you.

Prompt 5: The “Generate Body” Commit

For significant changes, a single subject line is often insufficient. A detailed commit body is essential for documenting complex logic, trade-offs, and implementation details. This is especially true for open-source projects or teams with high turnover, where the “why” behind a decision is as important as the code itself.

While many developers dread writing these manually, AI excels at this structured task. This prompt breaks the process into two parts: a concise summary for the subject line and a detailed breakdown for the body. It leverages the AI’s ability to parse the diff and categorize the changes logically.

The Prompt: Create a detailed commit message with a subject line and a body. The subject should be a concise summary. The body should list the key technical changes and the reasoning behind them, based on the git diff.

This is the gold standard for commits related to performance optimization, security patches, or major refactors. The resulting message might look something like this:

feat: Optimize database queries for dashboard

- Replaced N+1 query for user permissions with a single `JOIN`.
- Added caching layer for `config` lookups to reduce Redis latency by ~40ms.
- This resolves the slow page load issue reported in ticket #1138.

Pro-Tip: This is where you add real value. While the AI can list the technical changes, you should always review the body and add a sentence about the business impact or link to the relevant issue tracker. This blend of AI efficiency and human context is the ultimate goal.

By mastering these three advanced prompts, you move beyond simple automation and start using AI as a strategic partner in maintaining a clean, informative, and actionable project history.

H2: Integrating Prompts into Your Cursor Workflow

So you’ve got a powerful prompt ready to go, but how do you actually weave it into your daily coding rhythm without breaking your flow? The magic of Cursor isn’t just its AI capabilities; it’s how seamlessly those capabilities can be summoned. The difference between a developer who dabbles with AI and one who achieves a true state of flow is often a matter of workflow design. Let’s transform those copy-pasted prompts into an automated, high-efficiency part of your Git process.

The Quick and Dirty: Using Cursor Chat for On-the-Fly Commits

This is the simplest entry point, perfect for when you’re in the zone and just need to get a commit message generated quickly. It requires no setup and becomes second nature after a few uses. The goal is to capture the context of your work immediately after you’ve written the code, while the changes are still fresh in your mind.

Here’s the step-by-step workflow I use dozens of times a day:

1. Stage Your Changes: Before you even open the chat, run git add . or selectively stage the files you just worked on in your terminal or Source Control panel. This is a crucial step.
2. Generate the Diff: Now, in your terminal, run git diff --staged. This command outputs the exact changes you are about to commit. This is your raw context.
3. Open Cursor Chat: Hit the shortcut (usually Cmd/Ctrl + L) to open the full-line chat.
4. Paste and Prompt: Paste the git diff --staged output directly into the chat, followed by one of your core prompts. For example:

   Analyze the git diff and generate a concise, professional commit message. Follow the format: "[Action]: [Specific Area/Function] - [Brief Description of Change]".

   [Paste your git diff output here]

5. Execute the Commit: Cursor will analyze the diff and generate a perfectly formatted message. Copy the output, close the chat, and paste it into your commit command: git commit -m "Refactor: auth.ts - Simplify token refresh logic".

This manual process takes about 15 seconds and guarantees your commit messages are always context-aware and granular.

Level Up: Creating Custom Slash Commands for Power Users

If you’re committing multiple times a day, the copy-paste method above, while fast, can still be streamlined. This is where Cursor’s custom commands (slash commands) become a game-changer. You can create reusable, parameterized prompts that you can invoke with a simple /command. It’s like building your own micro-tooling right inside your IDE.

Let’s create a /commit command that does all the heavy lifting for you.

1. Open Command Settings: Navigate to Cursor Settings > Features > Custom Commands.
2. Create a New Command: Click “Add Custom Command.”
3. Define the Command:
   • Name: commit
   • Prompt: This is the core logic. We can use a special variable {{diff}} that Cursor will automatically replace with the staged changes.

     Analyze the following git diff and generate a concise, professional commit message. Follow the format: "[Action]: [Specific Area/Function] - [Brief Description of Change]". Example: "Refactor: auth.ts - Simplify token refresh logic".

     Git Diff:

     {{diff}}

4. Save and Use: Save the command. Now, the workflow is even faster:
   1. Stage your changes (git add .).
   2. Open Cursor Chat.
   3. Type /commit and hit enter.

Cursor will automatically pull the staged diff, run your custom prompt, and give you the message. For even more power, you can create a /commit-conv command for a more conventional commit format (feat:, fix:, chore:, etc.), or a /commit-multi for handling complex, multi-file changes.

Golden Nugget: A common pitfall with custom commands is forgetting to stage your changes before invoking them. If you use {{diff}}, it pulls the staged diff. If you forget to git add, you’ll get an empty response or a diff of nothing, which can be confusing. Always stage first, then command.

The Non-Negotiable: Best Practices for Review and Edit

Here’s a hard-won lesson: AI is a junior partner, not a final authority. The most dangerous commit is one you never read. AI-generated messages are an incredible starting point, but they lack your intentionality and deep understanding of the why behind the change. The final 5% of the process is your responsibility.

Always treat the AI output as a first draft that requires your review. Ask yourself these questions before you push:

• Is it accurate? Does the message truly reflect the spirit of the change, not just the literal code modifications? Sometimes a “refactor” is actually a “fix” for a subtle bug.
• Is it too jargon-heavy? The AI might use internal function names that are meaningless to an external reviewer. Can you make it clearer?
• Does it tell the right story? A commit message like Fix: array index in user loop is technically correct but useless. A better version is Fix: Prevent crash on user profile page for accounts with no activity. The first describes the code; the second describes the user-facing problem you solved.

Adding that human touch takes an extra 10 seconds but elevates your project’s history from a simple log to a valuable, searchable knowledge base. It’s the difference between a commit history that tells you what changed and one that tells you why it mattered.

H2: Real-World Examples: From Messy Diff to Clean Commit

Theory is great, but nothing beats seeing the AI work in the trenches. The real power of Cursor’s context-aware generation is how it transforms a chaotic git diff—the raw, unfiltered output of your work—into a structured, professional history. Let’s walk through three common scenarios that often trip up developers: a critical bug fix, a new feature rollout, and a tricky refactor.

Case Study 1: The Bug Fix

Bug fixes are notorious for producing messy, multi-file diffs where the root cause is buried in a sea of unrelated changes. A human reviewer (or your future self) can easily lose an hour trying to decipher the “why.”

The “Before”: A Messy Code Diff

Imagine you discovered a race condition in your authentication flow. The fix involved changing a single function in auth.ts and adding a defensive check in UserSession.ts.

diff --git a/src/auth.ts b/src/auth.ts
index a1b2c3d..e4f5g6h 100644
--- a/src/auth.ts
+++ b/src/auth.ts
@@ -112,7 +112,7 @@ export async function refreshAuthToken(userId: string) {
   try {
-    const response = await api.post('/auth/refresh', { userId });
+    // Add a small delay to prevent race condition with concurrent calls
+    await new Promise(resolve => setTimeout(resolve, 100));
+    const response = await api.post('/auth/refresh', { userId });
     return response.data.token;
   } catch (error) {
     console.error('Failed to refresh token', error);
diff --git a/src/UserSession.ts b/src/UserSession.ts
index d4e5f6g..h7i8j9k 100644
--- a/src/UserSession.ts
+++ b/src/UserSession.ts
@@ -45,6 +45,9 @@ export class UserSession {
   }
 
   async getSession() {
+    if (!this.token) {
+      return null;
+    }
     return this.token;
   }
 }

A manual commit message might be fix auth bug. This is useless. It tells you what you did, but not what you fixed.

The “After”: Cursor’s Clean Commit

Using our core prompt, Analyze the git diff and generate a concise, professional commit message. Follow the format: "[Action]: [Specific Area/Function] - [Brief Description of Change]", Cursor analyzes the entire diff and produces:

Fix: auth.ts & UserSession.ts - Resolve race condition in token refresh

This is a 10x improvement. It immediately tells a reviewer that:

1. What: It’s a Fix.
2. Where: The scope spans auth.ts and UserSession.ts.
3. Why: The core problem was a race condition in token refresh.

A reviewer now knows exactly where to focus their attention without reading a single line of code. This is the difference between a 5-minute review and a 30-minute investigation.

Case Study 2: The New Feature

New features often involve multiple files and a clear user-facing goal. The challenge is connecting the technical changes to that goal. A commit like add user profile is too vague. What about the profile? What was added?

The “Before”: A Multi-File Feature Diff

Let’s say you’ve added a user profile page. This required creating a new component, adding an API endpoint, and updating the routing.

diff --git a/src/components/ProfilePage.tsx b/src/components/ProfilePage.tsx
new file mode 100644
index 0000000..a1b2c3d
--- /dev/null
+++ b/src/components/ProfilePage.tsx
@@ -0,0 +1,25 @@
+import React from 'react';
+// ... component logic for displaying user data ...
diff --git a/src/server/api.ts b/src/server/api.ts
index e4f5g6h..d7i9j0k 100644
--- a/src/server/api.ts
+++ b/src/server/api.ts
@@ -50,6 +50,10 @@ router.get('/users/:id', async (req, res) => {
   const user = await db.users.findById(req.params.id);
   res.json(user);
 });
+
+// New endpoint for fetching user profile details
+router.get('/users/:id/profile', async (req, res) => {
+  const profile = await db.profiles.findByUserId(req.params.id);
+  res.json(profile);
+});
diff --git a/src/App.tsx b/src/App.tsx
index h7i8j9k..k1l2m3n 100644
--- a/src/App.tsx
+++ b/src/App.tsx
@@ -20,6 +20,7 @@ function App() {
         <Route path="/dashboard" component={Dashboard} />
         <Route path="/settings" component={Settings} />
+        <Route path="/profile" component={ProfilePage} />
       </Router>
     </div>
   );

The “After”: Cursor’s Clean Commit

When you use the multi-file summary prompt, Summarize the changes across these multiple files into a single, coherent commit message. Focus on the overall user-facing impact..., Cursor connects the dots:

feat: Implement user profile page - Add new route, component, and API endpoint

This commit message is a complete story. It highlights the user-facing feature (user profile page) and details the technical implementation (new route, component, and API endpoint). This is exactly the kind of commit that makes git blame and git log --grep="profile" incredibly powerful tools for future development.

Case Study 3: The Refactor

Refactors are the hardest commits to write because, by definition, nothing should change from the user’s perspective. The value is in the code quality, but a message like refactor code is actively unhelpful. It raises red flags for reviewers: “Why did you touch working code?”

The “Before”: A Pure Refactor Diff

You’ve noticed duplicated logic for handling API errors across three different services. You extract it into a shared utility.

diff --git a/src/services/UserService.ts b/src/services/UserService.ts
index k1l2m3n..o4p5q6r 100644
--- a/src/services/UserService.ts
+++ b/src/services/UserService.ts
@@ -1,5 +1,5 @@
 import api from './api';
-import { User } from '../types';
+import { User, ApiError } from '../types';
+import { handleApiError } from '../utils/errorHandler';
 
 export async function fetchUser(id: string): Promise<User> {
   try {
@@ -7,9 +7,7 @@ export async function fetchUser(id: string): Promise<User> {
     return data;
   } catch (error) {
-    console.error('User fetch failed', error);
-    // ... other error handling logic ...
-    throw new Error('Could not fetch user');
+    handleApiError(error, 'fetchUser');
   }
 }

(Similar changes in OrderService.ts and ProductService.ts)

The “After”: Cursor’s Clean Commit

Our core prompt works perfectly here. Cursor recognizes the action (Refactor) and the specific area (error handling logic).

Refactor: error handling - Consolidate API error handling into a shared utility

This message is a gold nugget for your team. It immediately communicates:

1. Intent: This is a Refactor, not a feature or fix.
2. Benefit: The goal is to Consolidate logic.
3. Impact: It affects the error handling system.

A reviewer sees this and knows the change is safe, improves maintainability, and is exactly the kind of proactive work the team values. They can review it for correctness, not for accidental feature changes.

Conclusion: Elevate Your Git Game with AI

Let’s be honest: writing commit messages often feels like a chore, a task we rush through just to push our code. But we’ve all felt the frustration of trying to decipher a cryptic “fix bug” message from six months ago. By integrating AI-powered prompts into your workflow, you’re not just saving a few seconds per commit; you’re fundamentally changing your relationship with your project’s history. The core benefits are immediate and tangible: you reclaim valuable mental energy, your code reviews become faster and more focused, and you build a searchable, granular history that your entire team can rely on.

The goal here isn’t to replace your critical thinking. It’s to augment it. Think of these prompts as a co-pilot that handles the tedious formatting, allowing you to focus on the why behind your changes. A well-crafted commit message like feat: auth.ts - Add JWT refresh logic on 401 response is a story. It tells a future developer (or your future self) exactly what problem you solved and where to look. Adopting this practice is a small investment with a massive return in long-term maintainability.

“The best developers aren’t just writing code; they’re curating a legacy. AI-assisted commits are the most efficient tool I’ve found to build a clear, valuable project narrative.”

Looking ahead, the line between developer and tool will continue to blur. Tools like Cursor are not just autocomplete on steroids; they are context-aware partners. By letting AI handle the boilerplate and administrative tasks like commit messages, we free ourselves to tackle the truly complex, creative challenges: architecting elegant systems, solving novel business problems, and building the next generation of software. This collaboration is the future, and it starts with a simple, well-formed prompt.

Expert Insight

Frequently Asked Questions

Q: Why are generic AI commit messages insufficient

Generic prompts lack project-specific context, leading to vague outputs. Cursor’s unique advantage is its deep understanding of your entire codebase, allowing it to generate granular messages that accurately reflect complex changes

Q: How does this improve code review efficiency

Descriptive commits allow reviewers to understand the intent and scope of changes instantly, reducing the time spent deciphering diffs and focusing discussion on architectural impact rather than basic intent

Q: Can this workflow be automated in CI/CD

Yes. While this guide focuses on manual prompting for quality, the generated messages can be validated against pre-commit hooks or CI pipelines to enforce standards like Conventional Commits across the team