lat.md: Transform Any Project Code into a Queryable Knowledge Graph

Why Simple Docs Fail as Projects Grow

In small projects a README suffices, but when a codebase expands to thousands of files across multiple teams, three problems arise: the files exceed an AI model's context window, documentation becomes stale without a verification mechanism, and plain docs capture only what the code does, not why or the governing constraints.

Step 1: Build the Project Map

After installing lat.md, a single command in the project root creates a dedicated "map" directory. The interactive installer asks which AI tool (e.g., Claude or Cursor) you use, sets basic rules, and writes an instruction for the AI to consult the map before reading all files.

Step 2: Scan the Code

When started, lat.md performs a local code scan—no code is uploaded. It supports over 20 programming languages, identifies major units such as functions and classes, and records their relationships, ensuring the map faithfully reflects the actual code.

Step 3: Link Docs to Code

lat.md

lets you attach notes directly to specific code locations. You can also add special comments in the code that point back to the corresponding note. The built‑in check command scans these links; if a link is broken or a code segment lacks documentation, it immediately flags the issue, keeping notes and code narrating the same story.

Step 4: "Summary First" Rule

Each documentation chapter must start with a brief summary of a few sentences. This rule enables the AI to quickly assess relevance when searching the map. If a chapter lacks a summary, the check command marks it, guaranteeing documentation quality and smoother navigation for both humans and AI.

Step 5: Automated Verification

Documentation often stalls because it is hard to keep up‑to‑date. lat.md integrates a verification step into the daily development workflow: on every code save, it automatically scans for broken links or missing notes and blocks the save until issues are resolved, preventing the map from becoming stale or inaccurate.

Getting Started

You don’t need to document the entire project at once. Begin with critical parts such as user login or data persistence logic, run the installer, let the AI help generate the first notes, and then enable automatic verification.

While lat.md is fast, language‑agnostic, and still evolving—occasionally struggling with extremely complex codebases—it delivers organization and consistency that traditional notes cannot achieve for most projects.

Conclusion

lat.md

moves you from merely searching for information to navigating an organized, verified project map. By attaching notes to code and enforcing automatic consistency checks, AI tools become smarter and more reliable, turning the map into the primary way the AI understands the project.