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Summary – Faced with the growing demand for fast, robust code, teams struggle to reconcile productivity and maintainability without getting bogged down in closed tools or unsynchronized workflows. Cursor AI leverages VS Code’s power by embedding a contextual chat, RAG indexing via @ syntax, Agent/Ask/Custom modes, background agents, and project rules to automatically generate, refactor, or audit each PR while ensuring consistency and security. The tool speeds up prototyping and code reviews, provided you limit the indexing scope, set strict rules, and track KPIs (generation time, acceptance rate, PR quality).
Solution: establish a versioned conventions repository, segment requests, and implement governance to deploy Cursor AI as a reliable digital teammate.
In a context where delivery deadlines and code quality pressures are constantly rising, IT teams are seeking tools to increase efficiency without sacrificing maintainability. Cursor AI presents itself as a VS Code–based code editor enriched by large language models to offer contextual chat, assisted generation, and editing directly within the development environment.
This article offers a comprehensive overview of Cursor AI: its origins, key features, real-world feedback, and best practices for integrating this tool into your processes. You will also learn when and how to leverage it, which safeguards to implement, and how to position it relative to other market solutions.
Overview of Cursor AI
Cursor AI is a VS Code fork optimized to integrate LLMs directly into the editor without leaving your workspace. It combines codebase indexing, an @ system to contextualize queries, and a chat capable of generating or editing code with deep project understanding.
Origin and Concept
Cursor AI leverages VS Code’s open-source architecture to provide a familiar editor for developers. While retaining all of Microsoft’s native editor features, it adds an artificial intelligence layer directly connected to the code.
This approach ensures an immediate learning curve: every VS Code shortcut and extension is compatible, enabling rapid adoption within teams. The flexibility of an open-source fork avoids vendor lock-in issues and allows for scalable customization.
The result is a hybrid tool where the classic text editor is enhanced by an assistant capable of interacting via chat, suggesting refactorings, and extracting relevant context in real time.
Contextual Chat and Interaction Modes
Cursor AI’s chat offers several modes: Agent, Manual, Ask, and Custom. Each serves a specific need, whether it’s an automated agent for PR review or a manual mode for finer ad hoc queries.
In Agent mode, the AI executes predefined tasks as soon as you push your code or open a branch, while Ask mode allows you to pose one-off questions about a specific code fragment. Custom mode enables the creation of project-specific workflows via a configuration file.
These modes provide fine-grained control over AI usage, allowing both automation for routine tasks and targeted intervention when code complexity demands it.
Codebase Indexing and the @ System
Cursor AI begins by indexing your entire codebase using an MCP (Multi-Code Parser) tool capable of understanding languages and frameworks. This index is leveraged by the @ system, which references files, internal documentation, and related web content.
When you make a query, the AI first draws from this index to build a rich and relevant context. You can explicitly point to a folder, documentation, or even a URL using the @ syntax, ensuring responses aligned with your internal standards.
This RAG (Retrieval-Augmented Generation) capability provides precise knowledge of your project, going far beyond simple code completion, minimizing errors and off-target suggestions.
Example: A Swiss SME in the services sector tested creating a task management application in a matter of minutes. With just a few commands, the team generated the structure of a todo app, including the user interface, persistence layer, and a basic test suite. This demonstration showcases Cursor AI’s efficiency in rapidly prototyping and validating a concept before embarking on more advanced development.
Key Features of Cursor AI
Cursor AI offers a range of built-in tools: deep navigation, code generation, background agents, and a rules system to control suggestions. These features are accessible via commands or directly from the chat, with an extension marketplace to expand capabilities and select the LLM that best suits your needs.
Navigation and Advanced Queries
Cursor offers commands like read, list, or grep to search code and documentation in an instant. Each result is presented in the chat, accompanied by context extracted from the index.
For example, by typing “grep @todo in codebase,” you get all the entry points for a feature to implement, enriched with internal comments and annotations. This accelerates understanding of a feature’s flow or a bug.
These queries are not limited to code: you can query internal documentation or web resources specified by @, ensuring a unified view of your sources of truth.
Code Generation and Editing
Cursor AI’s chat can generate complete code snippets or propose contextual refactorings. It can fill out an API endpoint, rewrite loops for better performance, or convert snippets to TypeScript, Python, or any other supported language.
The MCP mode also allows you to execute terminal commands to create branches, run generation scripts, or open PRs automatically. The editor then tracks the results, suggests corrections, and creates commits in real time.
You can thus delegate the initial creation of a feature to Cursor AI, then manually refine each suggestion to ensure compliance and quality, while saving several hours of development time.
Agents and Rules System
With Cursor Rules, you define global or project-specific rules: naming conventions, allowed modification scope, documentation sources, and even patch size limits. These rules automatically apply to AI suggestions.
Background agents monitor branches and pull requests, perform automated code reviews, and can even suggest fixes for open tickets. They operate continuously, alerting developers to non-conformities or detected vulnerabilities.
This system allows you to deploy AI as a permanent collaborator, ensuring consistent quality and coherence without manual intervention for every routine task.
Example: A Swiss fintech configured an agent to analyze each pull request and enforce security rules. Within a few weeks, it reduced manual vulnerability fixes by 40% and accelerated its review cycle, demonstrating the value of a dedicated security agent.
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Feedback and Use Cases
Multiple companies have experimented with Cursor AI for prototypes, POCs, and code review workflows, with mixed results depending on project size and rule configuration. This feedback highlights the importance of defining clear scope, limiting the context window, and tuning the model to avoid drift and incoherent suggestions.
Prototyping and POCs
In the prototyping phase, Cursor AI stands out for its speed in generating a functional base. Front-end teams can quickly obtain a first version of their UI components, while back-end teams rapidly get rudimentary endpoints.
This enables concept validation in a few hours instead of several days, providing a tangible foundation for stakeholder feedback. The generated code primarily serves as a structural guide before manual reliability and optimization work.
However, beyond about twenty files, performance optimization and overall style consistency become more challenging without precise rules.
Safeguards and Limitations
Without well-defined rules, the AI may propose changes beyond the initial scope, generating massive pull requests or inappropriate refactorings. It is therefore imperative to restrict change size and exclude test, build, or vendor folders.
The choice of LLM also affects consistency: some models generate more verbose code, while others focus on performance. You should test multiple configurations to find the right balance between quality and speed.
On large repositories, indexing or generation delays can degrade the experience. A reduced indexing scope and an activated privacy mode are solutions to ensure responsiveness and security.
Example: A Swiss industrial company conducted a POC on its multi-gigabyte monolithic repo. The team observed generation times of up to 30 seconds per request and off-topic suggestions. By segmenting the repository and enforcing strict rules, they reduced these times to under 5 seconds, demonstrating the importance of precise configuration.
Quick Comparison
Compared to GitHub Copilot, Cursor AI provides a full-fledged editor and an agent mode for code review, whereas Copilot remains focused on completion. The two can coexist, but Cursor excels for automated workflows.
Windsurf offers an IDE with an integrated browser for full-stack workflows but remains more rigid and less modular than a VS Code fork. Lovable, on the other hand, targets complete web stack generation but relies on a sometimes costly credit system.
Ultimately, the choice depends on your priorities: open-source agility and customization (Cursor AI), GitHub integration and simplicity (Copilot), or an all-in-one ready-to-use solution (Lovable).
Best Practices and Recommendations
To fully leverage Cursor AI without compromising quality or security, it is essential to structure your usage around clear rules, segmented tasks, and precise tracking of productivity metrics. Dedicated governance, involving IT leadership and development teams, ensures a progressive and measurable rollout.
Define and Manage Project Rules
Start by establishing a repository of code conventions and action scopes for the AI: allowed file types, naming patterns, and patch size limits. These rules ensure the assistant only proposes changes consistent with your standards.
Integrate these rules into a common, versioned, and auditable repository. Every rule change becomes traceable, and the history allows you to understand the impact of adjustments over time.
Finally, communicate these rules regularly to all teams via a discussion channel or documentation space to maintain cohesion and avoid surprises.
Structure Sessions and Break Down Tasks
Breaking down a complex request helps limit the context window and yields more precise responses. Instead of asking for a global refactoring, favor targeted queries on one module at a time.
Organize short 15- to 30-minute sessions with a clear objective (such as generating an endpoint or updating a service class). This approach reduces the risk of drift and facilitates manual validation by developers.
For code reviews, enable the agent on feature branches rather than the main branch to control impacts and gradually refine the model.
Measure and Track Gains
Deploy key indicators: average generation time, number of suggestions accepted, volume of code generated, and quality of automated PRs. These metrics provide an objective view of Cursor AI’s contribution.
Integrate this data into your CI/CD pipeline or monthly reports to monitor productivity trends and detect potential drifts.
Finally, schedule regular review meetings with IT leadership and project teams to adjust rules, switch LLM models if necessary, and share feedback.
Boosting Developer Productivity
Cursor AI builds on VS Code’s legacy and LLM integration to deliver a modular, feature-rich editor capable of automating routine tasks. By combining contextual chat, RAG indexing, and background agents, it becomes a genuine digital teammate for your teams.
To fully capitalize on its benefits, establish clear rules, segment your queries, and track performance indicators. Our digital transformation experts can assist you with the implementation, configuration, and management of Cursor AI within your organization.
