Designing AI Context Layers in Cursor for Large Codebases

Designing AI context layers in Cursor for large codebases is a practice of systems thinking, as it requires engineers to maintain a selective interface between the model and a complex system. When designed well, it turns Cursor into a collaborator that can reason through thousands of files without being overloaded with unnecessary details.

Let us now look at the outcomes of this collaboration. Cursor’s AI agent boosts developers’ teams to 39% more weekly pull-request merges without revert spikes, according to a University of Chicago study of 24 organisations. Furthermore, specialists achieve 40% faster feature delivery, as demonstrated by an AI pair-programming study. This essay will provide a guide to designing these context layers and achieving the best results. 

Why do teams need context layers?

Loading entire codebases into LLM context windows often degrades performance. As a result, latency increases dramatically, costs rise, and accuracy deteriorates because irrelevant data noise overshadows relevant data. Layered context, on the other hand, delivers concrete results, as evidenced by empirical studies. They can be materialised when developers deliberately select the input that the AI receives. 

These successful results stem from a fundamental limitation: tools such as Cursot cannot process large repositories (10,000+ files) in a single prompt. Instead, the tools automatically include the current file, recent edits, linter errors, and semantic research results, limiting individual file analysis to approximately 250 lines. Therefore, without deliberate file selection via commands such as @file or @code, AI accesses only a small percentage of data and is likely to produce irrelevant outputs. Specifying exact files rather than @codebase providescomplete context for accurate results.

Cursor’s native context model

Let us now discuss the Cursor’s model itself. It implements a two-tier context architecture: 

• Intent context. That is, a user’s prompt describing the task (such as “migrate authentication from JWT to session tokens”) 
• State context. Recent files, edits, semantic search, and linter errors.

Two main interaction modes are embedded into the Cursors’ model: 

• Chat mode. It functions as a project-aware conversational assistant with the following features: @codebase for broad repository scans or more targeted @file, @code, @git, and @docs references.
• Agent Mode. It handles complex tasks autonomously, such as executing commands, creating/modifying files, and troubleshooting. The mode uses retrieval to identify relevant files beyond those manually specified. 

Main design principles

From a practical perspective, successful context engineering in Cursor includes four interdependent principles: scoping, selectivity, structure, and stability. The following list will discuss them one by one. 

1. Scoping. It is crucial to specify engineering intent. For instance, a request to “fix a buggy integration test” should be drawn in only the relevant data model and test case and not the entire service directory. 
2. Selectivity. Targeted context selection, such as @code for specific functions or @file for discrete modules, yields superior performance compared to comprehensive @codebase retrieval, particularly in response latency, output accuracy, and reduced hallucination rates.
3. Structure. Cursor’s semantic search performs best on codebases with consistent naming conventions (e.g., FooService, FooRepository, FooController) and well-defined module boundaries.
4. Stability. Persistent project rules in Cursor establish a global context layer that is injected into every interaction. It embeds non-negotiable architectural conventions, such as error-handling patterns and logging standards.

Layering strategies 

Productive AI usage in large codebases requires applying a hierarchy of context layers. This hierarchy consists of global rules (persistent architectural conventions), project summaries (high-level system overviews), task-specific references (files, functions, or diffs relevant to the current objective), and Cursor’s automatic enrichment (recent edits, semantic search results, and diagnostics). Organising context in this layered vein ensures that the model operates within stable constraints and focuses solely on the elements necessary for the current task. Let us have a closer look at them:

• Global and project rules. At the other layer, define invariants that apply to various components of the repository. For example, all external API calls implement exponential backoffs with jitter and database migrations follow the standard naming convention. They are persistent guardrails that ensure consistency without the need for repeated instructions. 
• Project notepads and high-level summaries. Maintain @docs-accessible summaries of the crucial subsystems, such as domain models or migration strategies. This is because large architectural documents should be summarised to maintain semantic meaning by utilising as few tokens as possible. In turn, these summaries provide system-level awareness without exposing unnecessary implementation detail.

• Task-level context. It includes shortcuts such as @git for commit history and @docs or @web for framework documentation, as well as external sources. For example, a billing refactor could include @code (payment handler), @file (billing model), @git (recent schema changes), and a project notepad summary.
• Agent mode with intentional guardrails. For cross-service changes, combine clear architectural intent with a prompt, 2-3 reference implementations, and access to high-level summaries. Agent Mode can then identify dependencies via semantic search, speeding up large-scale refactoring.

Case studies

The topic of contextual layering is timely, and, as outlined above, its use has been on the rise. For instance, in enterprise monorepositories, massive merge requests are managed by decomposing diffs into semantic chunks. They are then analysed independently with only their local modifications and the minimal architectural context required for accurate interpretation. 

One recent case showed that tools like CRken use this approach to enable simultaneous review of 100+ files. The same approach applies to Cursor, which focuses on separate changesets and their dependencies to maintain scalability and performance during large-scale refactoring. 

Implementation roadmap

Having covered each aspect of the AI context layering, in a nutshell, these are the steps to follow:

1. Standardise naming, module boundaries.
2. Include one-page subsystem overviews and architectural invariants.
3. Create task-scoping templates and review AI outputs.
4. Measure impact by tracking it via Cursor Analytics.

When integrating this framework, it is important to recognise that Cursor’s true value lies in deliberate layering rather than token constraints. 

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