Making Cursor Work for You: The Power of Atomic Planning

A practical guide to working smarter with Cursor as your project grows.

Tell me if this sounds familiar: You're blown away by your AI coding assistant at first, only to later find yourself yelling “why don’t you get it?” at the screen as your project grows. You are not alone! Many developers start off amazed with AI code assistants like Cursor, but hit a wall when their codebase becomes large and the agent's helpfulness takes a nosedive. The good news is that this isn’t an unsolvable flaw – it’s a signal to change how you are using the tool. This is where the atomic planning method – a fancy term for breaking tasks into small, focused units – changed everything for me. In this post, I want to explain what atomic planning means for AI-assisted coding, why tools like Cursor struggle with context overload, and how shifting your approach can restore "AI magic", even in complex projects.

What is Atomic Planning in AI-Assisted Coding?

Atomic planning means structuring your work into the smallest viable pieces – “atoms” of tasks – and tackling them one by one. In the context of Cursor, it boils down to keeping each session very focused on a single, well-defined task, rather than a vague or sprawling request. Think of it like assigning tasks to a junior developer: you wouldn’t tell a newbie to build out your entire authentication system in one coding session. Instead, you’d tell them to break the project into smaller tasks (create a login function, then a signup flow, then integrate the database calls, etc.) and guide them step by step. We should treat our AI coding partners the same way. In fact, I use Cursor only for atomic tasks. By keeping each session narrow in scope, I'm reducing ambiguity and giving the model a clear target to hit.

In practical terms, atomic planning with Cursor involves a few mindset shifts:

One Task, One Session: Tackle features or fixes in isolated steps. For example, instead of one session for “Implement dark mode in this app,” break it down: “Add a dark mode toggle button to the settings page UI.” Once that’s done, next session: “Use a CSS class to apply dark theme styles when the toggle is on,” and so on. Each session addresses a single objective.
Stay Focused: Provide only the relevant context for the currently active task. Don’t stuff the entire codebase or a whole design spec into the session if you only need one function changed. The agent performs best when it’s not drowning in unrelated information.
Disable Auto-Run, Stay in Control: Turn off auto-run mode in Cursor and manually approve each agent response. This gives you the chance to read and evaluate the agent's suggestions before any changes are made. It helps you spot when the agent starts to drift off-track, avoids unnecessary file edits or creations, and keeps your context window clean. Plus, it saves tokens—both in memory and in cost.
Iterate and Verify: After the agent completes each task, verify the output (run tests, check the code, etc.). Then proceed to the next task. This gives you a chance to catch misunderstandings early and correct course, and it reinforces the AI’s progress by keeping the recent context clear and on-point.

By planning atomically, you’re effectively project-managing the agent: you feed it a to-do list one item at a time. This approach dramatically improves the quality of AI-generated code because it aligns with how large language models work. The more specific and contained the session, the more likely the AI will nail it. Plus, breaking down tasks forces us as developers to clarify our requirements.

When AI Overloads: Why Cursor Struggles as Projects Grow

So why does Cursor start acting wonky on larger codebases? The core issue is context overload. Large Language Models like those powering the Cursor agent have a limited “context window” – essentially, the amount of code and conversation history they can pay attention to at once. Cursor, in particular, is an AI-enhanced IDE (built on VS Code) that indexes your entire project to answer questions and apply changes across files. That’s powerful, but the agent's working memory has limits. Feed it too much, and it starts forgetting what it just read.

Models do great when the scope is limited, but they get confused trying to keep track of a huge codebase all at once. When you feed the AI too much at once—say, multiple files worth of code, a long thread of instructions, or a ton of active Cursor rules—it may start to forget earlier details, ignore parts of the input, or mix things up. This is known as “Context Window Overflow,” where coherence breaks down as you exceed what the AI can reliably handle. This may result in the AI going off on tangents, misremembering prior code, or repeat the same failed solution because it doesn’t fully recall your past instructions.

Context overload in Cursor often shows up in familiar (and frustrating) ways:

The Agent "Forgets" What It Did – You implement feature A with Cursor’s help, then move on to feature B. While working on B, the agent suggests changes that break feature A or reimplements A from scratch. It’s not intentionally sabotaging you; it simply doesn’t have A’s details top-of-mind anymore.
Duplicate or Inconsistent Code – When the agent isn’t aware of the full context, it might create duplicate functions or conflicting implementations. For example, it might write a new helper function that does something almost identical to an existing one in another file that scrolled out of context.
Stuck in a Loop of Fixes – Another symptom is the agent repeatedly offering the same fix or explanation without actually resolving the problem. It isn't learning from its failures or truly remembering its past attempts, because each attempt pruned the earlier context out. This kind of “Groundhog Day” debugging is a clear sign the agent is over capacity in understanding the big picture.
General Degradation of Quality – You might notice that responses get less relevant or more nonsensical as a session goes on. Long chats can lead to Context Degradation, where the agent's coherence breaks down over time. Essentially, the farther you go without resetting or refocusing, the more the agent starts to lose the plot.

It’s important to note that these issues aren’t because Cursor (or any AI code assistant, for that matter) is inherently bad – they stem from how current AI models work. These models just don’t have an infinite memory or perfect reasoning over huge contexts yet. Sure, you can buy bigger context windows, but even those can struggle beyond a certain point (tens of thousands of tokens). In short, as your project scales up, you need to adjust your approach or the AI will start to flounder.

If you spend any amount of time of the Cursor subreddit, you'll see a steady stream of users complaining about a honeymoon period followed by disappointment. Early on, it’s amazing – but the features that make it feel magical on small tasks (e.g. looking at your whole project) can become a liability when the project outgrows the model's capacity. The key realization here is that we have to change how we work with the AI once we reach that point. We can’t expect the same strategy to work on a 50-file application as it did on a smaller prototype or demo. This is exactly where atomic planning can help.

How Atomic Planning Solves Context Adversity

The atomic planning method is like a reset button for the Cursor agent's effectiveness. By breaking big tasks into little ones, you essentially prevent the agent from ever getting overwhelmed in the first place. Instead of trying to force the model to swallow the ocean, you feed it a spoonful at a time. This has several powerful effects on the agent’s output and your overall workflow:

Keeps the agent’s "Attention" Focused: When each session is a small, self-contained task, the model only has to keep a limited context in mind – usually just the few files or functions relevant to that task and the latest instructions and rules. This means it’s much less likely to drop important details or confuse itself. In practice, that might mean if you have a feature touching 10 files, you handle it in two or three sessions instead of all at once. The AI will have a better shot at success with the smaller chunks, and you won’t spend hours debugging a garbled mega-diff.
Reduces Ambiguity and Errors: Smaller tasks are naturally more specific, leaving less room for the AI to misinterpret what you want. An instruction like "Refactor the calculateTotal() function to use decimal math for currency" is hard to misunderstand, whereas "Make the e-commerce app more robust" could go in a million directions. By decomposing complex changes into atomic, checkable units, you remove ambiguity and provide clear success criteria. Atomic tasks leave little room for misinterpretation – the AI either does the thing or it doesn’t, and both you and the model can clearly tell if the step is completed correctly.
Prevents "Context Amnesia": By resetting the context for each new session (or at least keeping it limited to the essentials), you avoid the scenario where the AI forgets what it did earlier. You can even periodically remind it of the high-level plan. For example, start a session with a brief summary: "We implemented X and Y already, now we’re doing Z." This keeps the recent conversation history aligned with the current task. Some Cursor power-users take this further by maintaining a running notepad of goals or a task list in the context (like a TASKS.md file) so the AI always has a short-term roadmap. By continually updating the AI on what’s done and what’s next, you ensure it doesn’t inadvertently undo previous work or wander off. The AI can then recap or mark off each sub-task as completed before moving on. In fact, making the AI explicitly recap the plan after each step is a great technique.
Gives You Control and Insight: When you work one small task at a time, you regain a sense of control over the AI’s contributions. Instead of unleashing a giant code change and crossing your fingers, you’re in the driver’s seat, guiding the AI. This turns the coding process into a dialogue: "AI, do this.” (Check result.) "Great, now do the next thing." If the AI makes a mistake, you catch it early and correct the course before it snowballs. It’s much easier to pinpoint where things went wrong in a step-by-step process than in a monolithic code dump. Plus, this approach naturally produces a trail of documentation – each prompt-response pair is like a commit message describing a logical unit of work. Some AI-first workflows even formalize this: using tools to generate task checklists that the AI then executes item by item.

By now, the message is clear: when the AI struggles, shrink the task. You’ll be amazed how often the “dumb” suggestions transform into smart ones once the request is narrowly defined. It flips the script from “Cursor is failing to do this complex thing” to “Cursor succeeds at doing this simple thing – and we’ll just do it 20 times.” Those small wins add up quickly. It’s a bit like refactoring your own approach to coding with AI – instead of expecting a one-shot miracle, you iteratively build the solution with the Cursor's help. This is exactly how we deal with complexity in software engineering (think agile sprints, modular design, writing one unit test at a time). With AI, we just have to be even more deliberate about it.

Examples: Atomic Planning in Action

Let’s ground this in a concrete scenario or two, to see how atomic planning makes a difference:

Feature Implementation Example

Suppose you’re adding a new feature to an app – say a “dark mode” toggle. A naïve approach would be to tell Cursor’s agent something like: “Add dark mode support to the whole app.” If the codebase is large, the agent might try to modify theme variables in dozens of places, possibly forget a spot, or alter something unrelated. Instead, with atomic planning, you break it down into multiple sessions:

UI Task: “Add a dark mode toggle switch in the settings panel (in Settings.jsx). Make it visually consistent with existing UI.” – The AI writes the JSX/HTML and basic styling for the toggle.
State Management Task: “When the toggle is clicked, update a theme state variable and store the user’s preference (you can use localStorage or a cookie).” – The AI implements the onClick handler, maybe introducing a new context or Redux state as needed.
Styling Task: “Apply dark theme styles when theme === 'dark'. Use the existing theming system (e.g., swap CSS variables or apply a dark-mode class to the body).” – The AI now focuses just on the CSS/JS needed to switch colors, perhaps guided by a ThemeProvider if one exists.
Persistence Task: “On app load, check the stored theme preference and initialize the theme state accordingly, so the choice persists.” – A small initialization code change.

By the end, you’ve guided Cursor through four small, focused sessions instead of a giant one-shot prompt. At the end of each task you could test or review the changes. The AI didn’t once try to rewrite your entire css file or forget what the toggle was supposed to do, because each session was narrowly focused. This aligns with advice from Cursor’s own docs: tackle each feature separately when requirements are extensive. The outcome is a working dark mode implemented piece by piece, with far less back-and-forth or weird surprises.

Bug Fix Example

Imagine debugging an issue where a fix involves multiple steps (like update an API endpoint, adjust the data model, and then modify the UI). If you tell the AI, “Fix the payment processing bug,” it might focus only on one layer (say, it adjusts the backend but doesn’t realize the frontend also needs updating, or vice versa). Instead, break down the bug fix:

Prompt about the backend change needed, implement it.
Then separately prompt about the frontend change needed as another task.
Then prompt to run tests or verify output (if you have that capability).
Finally, prompt for any cleanup (remove debug logs, etc.).

Developers often report that AI is good at fixing one thing at a time, but if the fix requires coordinated changes in many places, you have to be the one to coordinate it. By spoon-feeding the fixes, you prevent the AI from missing a piece. Plus, after each fix, you can confirm the bug’s status (maybe run your test suite). This is akin to doing test-driven development with the AI: you present one failing test or issue, solve it, then move to the next.

These examples highlight a pattern: the clearer and smaller the ask, the better the AI’s response. By planning out a series of micro-tasks, you turn a potentially overwhelming request into a sequence of straightforward ones.

Putting it into Practice: My Daily Workflow

Here’s a practical and repeatable workflow that I’ve found to be highly effective when using Cursor. This approach consistently produces excellent results—even in larger and more complex projects.

Step 1: Brainstorming in Chat Mode

I start every project by simply chatting with Cursor in Chat mode (without any editing or code-writing just yet). The goal here is twofold:

Clarify my own thinking: Discussing the project helps identify any hidden complexity, missing details, or new ideas I wouldn’t have thought of alone.
Give the agent comprehensive context: By thoroughly explaining my goals, expectations, and constraints, the agent develops a solid conceptual understanding of the entire project.

This conversational step is crucial. It’s the moment to ask open-ended questions like:

“What features or improvements might I be overlooking?”
“Are there potential performance issues we could proactively solve?”
“What edge cases haven’t I considered?”

You’ll often find the agent brings up important points you might have missed on your own.

Step 2: Creating a Comprehensive Planning Document

Once the project’s goals and constraints are clear and both you and the agent are aligned, I switch Cursor into Agent mode, which allows it to directly edit and generate project files.

I then explicitly instruct the agent:

“Create a detailed, comprehensive planning document outlining the project’s structure, requirements, features, and any key considerations. Assume this document will be handed off to an AI code assistant to guide the implementation.”

This explicit instruction achieves two key things:

Encourages clarity: The agent produces clear documentation aimed specifically at guiding future AI-assisted tasks.
Improves consistency: Any ambiguity gets resolved early, so subsequent tasks remain aligned with the overall project goals.

A good planning document includes:

An overview of the problem the project aims to solve.
Clearly defined features and functionality.
Technical requirements and considerations.
Suggested project architecture or key components.
Any anticipated challenges or risks.

Step 3: Generate Atomic Tasks

With the planning document complete, the next step is to translate it into actionable, atomic tasks. I instruct Cursor explicitly:

“Using the planning document you created, generate a series of atomic tasks. Each task should be clearly defined and scoped so it can be completed in 1–2 sessions using your own agent capabilities.”

This instruction ensures tasks are:

Well-defined: Each task has a clear start and finish, leaving minimal ambiguity.
Focused and small: Designed to avoid overwhelming Cursor’s context window.
Self-contained: Each task is achievable independently, maintaining coherence across sessions.

But I take it one step further. I also ask the agent to include a "Code Context" section for each task:

“For each task, identify which files are relevant from the existing codebase so the agent knows where to focus.”

This helps Cursor stay sharp and efficient by:

Narrowing its initial attention to the files that matter most.
Avoiding unnecessary project-wide scans.
Reducing memory strain and potential context overflow.

A well-scoped task with code context might look like this:

Task:
Implement the backend API endpoint to validate user credentials against the database.

Code Context:

routes/auth.js – define the new POST endpoint
controllers/userController.js – add validation logic
utils/validators.js – reuse or add input validation helpers
models/User.js – ensure schema compatibility

By bundling the "what" with the "where," I give Cursor’s agent the guardrails it needs to act confidently and accurately, without making incorrect assumptions or drifting into unrelated parts of the codebase.

Step 4: Execute and Iterate

Now you can confidently execute these tasks with Cursor’s agent mode, knowing each task is manageable and clear. Because each task is atomic, testing and verification become straightforward. Any issues can be addressed swiftly, minimizing disruption to the overall project.

The result is less frustration, fewer errors, and significantly better results from Cursor. By following this structured workflow, I am making Cursor my coding partner, maximizing the effectiveness and productivity of each session.

Better Prompts, Better Outcomes: It’s About How You Use the Tool

The key takeaway here is that by adjusting your approach working with Cursor (or any AI code assistant), you'll avoid a lot of frustration. Instead of assuming the tool doesn't work on large projects, try adjusting the way you interact with it.

Sure, current AI models have limitations, but as developers we’re used to working within constraints. Think of the AI’s context limit as just another constraint to design around. Reframing your interaction through atomic planning is essentially a design pattern for AI usage. It plays to the AI’s strengths (detail-oriented code generation and speedy edits) and circumvents its weaknesses (lack of long-term memory and holistic understanding).

Once I starting using atomic planning for every project, I understood that Cursor was a coding partner that needed some guidance. It has since made my workflow significantly more productive with much less repetition (and headaches).

Tips for Atomic Planning

To implement atomic planning effectively in your AI-assisted development:

Preparation

Start with Conversation: Begin projects in Chat mode to clarify thinking and brainstorm with the agent
Create a Planning Document: Generate a comprehensive document outlining structure, requirements, and features
Break Down into Atomic Tasks: Translate the plan into small, self-contained tasks with clear scope
Specify Code Context: For each task, identify which specific files are relevant

Task Definition

Be Hyper-Specific: One focused change per prompt ("Implement login validation" not "Build auth system")
Define Clear Boundaries: Each task should have obvious start/end points
Include File References: Tell the agent exactly where to focus (e.g., "Update auth.js and userController.js")
Set Success Criteria: Define how you'll know when the task is complete

Effective Communication

Start with a Plan: Tell the agent "We'll do this in X steps" before diving in
Request Step-by-Step Outlines: Ask for a solution plan before any coding begins
Check Off Progress: Use a TASKS.md file that both you and the agent can reference
Remind of Context: Brief recaps like "We've done X and Y, now let's do Z"

Execution Strategies

Control the Process: Manually approve each response rather than using auto-run
Verify Then Continue: Test each atomic change before moving to the next task
Reset Regularly: Start fresh sessions when the context gets cluttered
Iterate Confidently: Address issues immediately rather than pushing through multiple tasks

By adopting atomic planning practices, you’ll likely find that the AI’s output quality jumps and your frustration level drops. The development process becomes a more interactive, collaborative experience. Instead of wrestling with the AI, you’re coaching it.

Conclusion: Make Cursor a Productive Coding Partner

Working with Cursor on a complex codebase can feel like trying to have a conversation in a crowded, noisy room. Messages get lost, context gets confused, and misunderstandings lead to frustration. Atomic planning is like moving that conversation to a quiet space and talking through one point at a time. It’s a simple shift, but the benefits pay incredible dividends.

The overarching lesson is that AI tools amplify whichever approach you bring. If you throw undefined or ambiguous requests at it, you’ll get unsatisfying results. As developers, many of you have mentored junior devs; now we need to apply that skill to our AI assistants.

So next time you feel Cursor (or Copilot or any AI code assistant) is “letting you down”, take a step back and break the problem into atomic tasks. Feed them one by one to the agent and it will methodically solve each piece. Not only will your project get back on track, but you’ll likely save your own sanity and even learn to think about the problem more clearly yourself. In the end, you’ll spend less time fighting the agent and more time building cool stuff together. And that’s really the promise of these AI tools – a partnership where we provide direction and judgment, and the AI provides speed and precision. Atomic planning just helps fulfill that promise when things get complicated.

Give it a try on your next project, feature or refactor. You might just find that your AI pair programmer is a lot more capable than you thought – it just needed tasks it can handle. With a bit of atomic planning, you’ll turn those late-stage AI frustrations into a productive workflow, and you can get back to enjoying the process of coding with AI, even as your codebase grows. Happy coding, one small step at a time!