Is Documentation Dead? The Rise of Self-Explaining Code

For decades, developers have lived by the mantra: “Read the Manual” (RTFM). Documentation was the lifeline—the only way to understand how a library worked or how a legacy codebase was structured.

But let's be honest: Nobody likes writing documentation, and nobody likes reading outdated documentation.

In the era of Vibe Coding, where AI agents can ingest an entire repository and explain it in plain English, we have to ask: Is documentation dead?

The Death of “Static” Docs

The traditional model of documentation is broken.
1. It's always out of date: Code changes faster than the wiki.
2. It's disconnected: You have to leave your IDE to read it.
3. It's generic: It explains the general case, not your specific edge case.

With tools like Cursor, Windsurf, and Gemini 1.5 Pro, the need for static, hand-written documentation for implementation details is vanishing.

Why read a 50-page API reference when you can just type:
> “How do I use the `AuthService` to log in a user with 2FA? Show me an example.”

The AI reads the actual code (the source of truth) and gives you an answer that is 100% accurate to the current state of the repo.

The Shift: “Ask the Codebase”

We are moving from a Push model (authors push docs to readers) to a Pull model (readers pull answers from the code via AI).

* Old Way: Search Confluence for “Database Schema”, find a diagram from 2019, realize it's wrong, cry.
* New Way: Ask AI: “Generate a mermaid diagram of the current User and Order tables.” -> Get an instant, accurate diagram.

What Still Needs Documentation?

So, can we delete the `docs/` folder? Not quite.

While AI is great at explaining what the code does and how it works, it often struggles with why.

1. Architecture & Design Decisions

Why did we choose Microservices over Monolith? Why are we using MongoDB instead of Postgres? AI can't guess the historical context or the business constraints behind these decisions. Architecture Decision Records (ADRs) are more important than ever.

2. High-Level Intent

What is this module supposed to do? If the code is buggy, the AI will explain the buggy logic perfectly. It needs a high-level description to know if the implementation matches the intent.

3. Public APIs

If you are building a library for others (e.g., an npm package), you still need docs. Your users might not have access to your source code, or they might not be using an AI IDE.

AI-Generated Documentation

For the stuff that does need to be written (function headers, API specs), AI should be doing the heavy lifting.

The “Auto-Doc” Workflow:
1. Highlight a complex function.
2. Command: “Add JSDoc comments explaining parameters and return types.”
3. AI generates perfect, standard-compliant comments.

This keeps the code “self-documenting” without the developer typing a single word of description.

The Future: Interactive Walkthroughs

Imagine onboarding a new developer. Instead of handing them a `README.md`, you hand them an AI Agent.

> New Dev: “Walk me through the authentication flow.”
> Agent: “Sure. It starts here in `auth.controller.ts` (opens file). The request is validated by this middleware (highlights code). Then it calls `AuthService.login`…”

This isn't science fiction. It's what we do today with Mission Control and Context-aware chat.

Conclusion

Documentation isn't dead, but it is evolving. We are shedding the dead weight of manual, maintenance-heavy docs in favor of dynamic, AI-driven explanations. The future of documentation is conversation.

At BYS Marketing, we prioritize “Why” documentation over “How” documentation, relying on advanced AI tools to handle the rest.

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Want to modernize your development practices?
Contact BYS Marketing. We help teams transition to AI-first workflows.