grace-explainer

Prevent context drift in LLM code generation by applying a contract-driven structure with explicit semantic anchors like START_BLOCK and END_BLOCK markers, ensuring agents consistently locate and update correct code sections across sessions for deterministic outputs.

Make a codebase navigable for AI agents by inserting semantic markup that splits code into approximately 500-token anchors. Using START_BLOCK and END_BLOCK markers along with module maps allows agents to reliably locate, edit, and trace logic within the source files.

Contract-driven development for AI code generation is a methodology ensuring code is specified before implementation using MODULE_CONTRACT and function-level contracts. This structure drives deterministic generation and prevents AI assistants from producing hard-to-maintain code.

Create a knowledge graph for RAG-enabled agents by maintaining a single docs/knowledge-graph.xml file that maps modules, exports, dependencies, and CrossLinks. This always-current file provides the semantic structure required for accurate RAG retrieval.

Yes, you can use contract-first generation for project onboarding by generating a development plan, scaffolding module contracts, and producing semantic-marked source files that AI agents can safely update throughout the development lifecycle.

The limitation of semantic anchors is that they require maintaining block-level markers and a synchronized knowledge graph to remain effective for AI agents. Without consistent updates to the contract-driven structure, agents may lose navigation accuracy and context.