Large-Scale Codebase Management

Sypha is engineered to operate across repositories of any scale. However, massive codebases require specialized strategies to manage the AI model's context window effectively, ensuring high-speed reasoning and accurate logic synthesis.

Understanding Context Constraints

Sypha utilizes elite models with finite "context windows." This window is consumed by:

• The persistent system prompt and historical interaction logs.
• The raw source content of every file mentioned using the @ system.
• The generated output from terminal commands and automated tool execution.

Strategic Context Governance

1. Prioritize Specification: Reference exact file paths and method identifiers to prevent irrelevant context from bloating the prompt.
2. Leverage Surgical Mentions: Utilize @/path/to/asset.ts for direct file access and @problems to pinpoint active environment errors.
3. Task Partitioning: Decompose massive features into a structured sequence of small, manageable sub-tasks.
4. Logical Summarization: Instead of injecting a thousand-line file, provide a high-level summary of the relevant logic blocks.
5. Session Recalibration: Initialize fresh conversation threads for unrelated tasks to purge obsolete context and sharpen the AI's focus.
6. Infrastructure Caching: Select providers that support Prompt Caching (e.g., Anthropic, OpenAI) to minimize latency and expenditure when working with large assets.

Implementation Example: Refactoring a Complex Asset

1. Exploration Phase: @/src/services/LargeService.ts Analyze the architectural dependencies of this module.
2. Surgical Refactor: @/src/services/LargeService.ts modernize the 'calculateRisk' method to utilize the new validation hook.
3. Iterative Validation: Review and commit small, incremental logic shifts rather than attempting a systemic overhaul in a single request.