Checking Docs Against the Code They Describe

Maintaining accurate documentation is one of the most persistent challenges in software engineering. When you are checking docs against the code they describe, you are fighting a natural entropy where logic evolves faster than the prose explaining it. This gap, often called documentation drift, leads to developer frustration, broken integrations, and wasted hours of debugging. By leveraging modern AI coding agents within an integrated workspace, you can automate the verification of your markdown files, API references, and READMEs against the actual source of truth in your repository.

The Cost of Documentation Drift

Documentation drift occurs when the implementation of a feature changes but the corresponding documentation remains static. This is not merely a cosmetic issue. In a professional environment, incorrect documentation results in tangible costs.

1. Increased Onboarding Time: New engineers rely on docs to understand the system. If the docs are wrong, their initial week is spent unlearning false information.
2. Support Overhead: For internal or external APIs, outdated examples lead to unnecessary support tickets and GitHub issues.
3. Security Risks: Documentation that suggests deprecated or insecure patterns because the latest patches were never documented can expose the codebase to vulnerabilities.

The primary reason for this drift is that documentation usually sits outside the standard testing pipeline. While unit tests fail when code breaks, the README remains green even if the function signatures it describes no longer exist.

Strategies for Checking Docs Against the Code

There are several manual and automated approaches to ensure your prose matches your implementation. Each has different levels of complexity and reliability.

Manual Peer Reviews

The most traditional method is including documentation in the code review process. When a developer submits a pull request, the reviewer checks if the associated docs were updated. While effective for small teams, this is prone to human error. It is easy to overlook a subtle change in a parameter type that invalidates a code snippet in a setup guide.

Doctests and Literate Programming

Tools like Elixir, Python, and Rust support doctests, where code examples inside comments are actually executed as part of the test suite. This ensures that the examples provided in the API reference are functionally correct. However, this only covers docstrings within the code files and does not typically extend to standalone markdown files or architecture overviews.

AI-Driven Verification

The most flexible modern approach involves using AI coding agents to scan your repository. Unlike static linters, an agent can understand the context and intent of a paragraph. It can read a tutorial in your /docs folder and compare it against the logic in your /src folder, identifying discrepancies in logic, naming conventions, or installation steps.

Using AI Agents for Validation

To effectively use an agent for checking docs against the code, you need an environment where the agent has full access to the file system and the ability to run diagnostic commands.

Several agents are capable of this task:

When running these agents, the workflow involves pointing the agent to a specific document and asking it to verify the claims made therein. For example, you might ask an agent to verify if the three steps listed in setup.md actually work by attempting to run those commands in a controlled terminal session.

Integrating Deska into the Workflow

Deska provides a specialized environment for these types of complex, multi-file tasks. Since Deska is a local-first desktop application, your code and sensitive documentation never leave your machine unless you are specifically interacting with an AI provider.

The infinite canvas in Deska allows you to arrange your documentation on one side and the source code on the other. You can open multiple panels to see the full context of the project.

Running Agents Side by Side

In Deska, you can run agents like Claude Code or Codex CLI in parallel panels. This is useful for cross-referencing. You can have one agent analyze the logic of a library while another agent checks the documentation for that library. Because Deska allows for side-by-side agent threads, you can compare their findings to ensure a higher degree of accuracy.

Using Ask Deska for Navigation

The Ask Deska assistant can help you manage the workspace while you focus on the technical validation. You can use voice or chat to tell Deska to open all markdown files in the repository alongside the main entry point of your application. This reduces the friction of context switching between the editor and the documentation.

Remote Monitoring

If you are running a long validation script or a comprehensive agent scan that takes several minutes, you can use the mobile app. Through a secure relay that requires no open ports, you can monitor the progress of your agents from your phone. This is particularly helpful for teams running large-scale documentation audits.

Best Practices for Accurate Docs

To make the process of checking docs against the code more efficient, consider the following structural changes to your project.

• Use clear, descriptive headings that match the module names in your code.
• Minimize the use of screenshots, which become outdated instantly. Use code blocks that an agent can parse and test.
• Keep a CHANGELOG.md file updated. Agents can use this as a reference point to see what specifically needs to be checked in the prose documentation.
• Store your documentation in the same repository as the code. This ensures they are versioned together and accessible to local coding agents.

Comparison of Validation Approaches

Different tools take different paths to solving the documentation problem.

While static linters are great for finding broken links, only an agent can tell you that the paragraph describing a "retry logic" is false because the code actually throws an error after the first failure.

FAQ

How to automate documentation testing?

You can automate documentation testing by integrating tools like Claude Code into your local workflow. By using an agent that has access to your terminals and files, you can script a process where the agent reads your markdown files and attempts to execute the code snippets or verify the logic against the source files.

Can AI agents find outdated code examples?

Yes, AI agents are particularly effective at finding outdated examples. By comparing the function signatures in your source code with the examples in your documentation, an agent can identify mismatches in parameter types, return values, or method names that would otherwise go unnoticed until a user tries to run them.

Is it safe to use AI for private documentation?

Security depends on the tool and the model. Deska is a local-first application, meaning your files stay on your machine. When using your own API keys for agents, the data is sent to the provider (like Anthropic or OpenAI) for inference. For highly sensitive projects, using local models with OpenCode ensures that your documentation never leaves your hardware.

Download Deska for Documentation Management

If you want to streamline the process of checking docs against the code they describe, Deska offers the flexible workspace you need. By combining an infinite canvas with powerful coding agents, you can maintain a high standard of technical accuracy without the manual drudgery.

You can download Deska for Mac, Windows, or Linux to start organizing your documentation and code in a single, local-first environment.