Designing an AI context layer with Cursors in a large codebase is an exercise in systems thinking because it requires engineers to maintain selective interfaces between models and complex systems. When designed properly, Cursors turn into collaborators that can reason over thousands of files without overloading them with unnecessary details.

Let’s take a look at the results of this collaboration. According to the University of Chicago, Cursor’s AI agent allows developer teams to increase their weekly pull request merges by 39% without experiencing undo spikes. study Consists of 24 organizations. Additionally, our specialists achieve 40% faster feature delivery, as demonstrated by AI pair programming. study. This essay provides a guide to designing these context layers and achieving the best results.

Why do teams need a context layer?

Loading the entire codebase into the LLM context window often results in poor performance. The result is significantly increased latency, increased costs, and decreased accuracy as noise from irrelevant data overshadows relevant data. On the other hand, layered contexts have concrete consequences, as evidenced by empirical studies. These can be achieved by developers intentionally choosing the inputs that the AI ​​receives.

These successful results stem from fundamental limitations. Tools like Cursot cannot handle large repositories (10,000+ files) with a single prompt. Instead, the tool automatically includes the current file, recent edits, linter errors, and semantic findings, and limits analysis of individual files to approximately 250 lines. Therefore, there is no need to select files intentionally using commands like: @file or @codeAI may access only a small portion of the data and produce irrelevant output. specify the exact file instead of @codebase Provides complete context for accurate results.

Native context model for cursors

Now let’s talk about the cursor model itself. It implements a two-tier context architecture.

• Context of intent. That is, a prompt for the user that explains the task (for example, “Move authentication from JWT to session tokens”)
• Context of the state. Recent files, edits, semantic search, linter errors.

Two main modes of interaction are embedded in the cursor model.

• Chat mode. It acts as a project-ready conversation assistant with the following features: @codebase For broad repository scans or more targeted scans @file, @code, @gitand @docs References.
• agent mode. Autonomously handles complex tasks such as running commands, creating/modifying files, and troubleshooting. Use this mode to search for related files other than manually specified files.

Main design principles

From a practical perspective, successful context engineering with Cursors involves four interdependent principles: scope, selectivity, structure, and stability. We will discuss them one by one in the following list.

1. scoping. It’s important to be clear about your engineering intent. For example, a request to “fix buggy integration tests” should only be rendered with the relevant data model and test case, not the entire service directory.
2. selectivity. Targeted context selection. @code specific features or @file Discrete modules offer better performance compared to comprehensive modules. @codebase Reduction in search, especially response delay, output accuracy, and hallucination rate.
3. structure. Semantic search for Cursors performs best in codebases with consistent naming conventions (FooService, FooRepository, FooController, etc.) and well-defined module boundaries.
4. stability. Cursor’s persistent project rules establish a global context layer that is injected into every interaction. It incorporates non-negotiable architectural conventions such as error handling patterns and logging standards.

Tiering strategy

To use AI productively in large codebases, a hierarchy of context layers must be applied. This hierarchy consists of global rules (persistent architectural rules), project summaries (high-level system overview), task-specific references (files, functions, or diffs that are relevant to your current purpose), and automatic cursor enrichment (recent edits, semantic search results, and diagnostics). Organizing the context in this hierarchical structure ensures that the model operates within stable constraints and focuses only on the elements needed for the current task. Let’s take a closer look at them.

• Global rules and project rules. Other layers define invariants that apply to different components of the repository. For example, all external API calls implement exponential backoff with jitter, and database migrations follow standard naming conventions. These are permanent guardrails that ensure consistency without the need for repeated instructions.
• Project notepad and overview. maintain @docs– Accessible overview of important subsystems, including domain models and migration strategies. This is because large architecture documents need to be summarized using as few tokens as possible to preserve semantic meaning. These summaries provide system-level awareness without revealing unnecessary implementation details.

• Task-level context. Contains shortcuts such as. @git commit history and @docs or @web Framework documentation and external sources. For example, billing refactorings can include: @code (payment handler), @file (billing model), @git (recent schema changes), and a Notepad overview of the project.
• Agent mode with intentional guardrails. For changes between services, combine clear architectural intent and prompts, a few reference implementations, and access to an overview. Agent mode speeds up large-scale refactorings by identifying dependencies through semantic search.

case study

The topic of context layering is timely and, as outlined above, its use is increasing. For example, in an enterprise monorepository, large merge requests are managed by breaking down differences into semantic chunks. It is then independently analyzed using only local modifications and the minimal architectural context necessary for accurate interpretation.

one of the recent case We have shown that tools such as CRken can review over 100 files simultaneously using this approach. The same approach applies to Cursors, focusing on discrete change sets and their dependencies to maintain scalability and performance during large-scale refactorings.

Implementation roadmap

We’ve covered each aspect of AI context layering, but in a nutshell, here are the steps to follow:

1. Standardize naming and module boundaries.
2. Include a one-page subsystem overview and architectural invariants.
3. Create task scoping templates and review AI output.
4. Measure impact by tracking through Cursor Analytics.

When integrating this framework, it’s important to realize that Cursor’s real value lies in intentional layering, not token constraints.

This article was contributed by Roman Rastiehaiev, Software Engineer at HiBoB. Roman has nearly a decade of experience building reliable backend systems across fintech, SaaS, and data-intensive platforms. Throughout his career, Roman has designed and maintained distributed services, real-time data pipelines, and mission-critical infrastructure. Roman has a strong interest in how AIOps can transform incident detection, predictive monitoring, and automated operations, with a focus on improving system resiliency, observability, and performance at scale.