AI-Written Docs Are Lying to You: How Bad Explanations Break Your App

TL;DR

AI-generated documentation and explanations often suffer from Plausible Misdirection: the text sounds authoritative and clear but contains subtle, factual errors regarding API contracts, parameter types, or execution constraints. This leads engineers to integrate features based on a false premise, directly injecting conceptual bugs that are difficult to debug because the code matches the (incorrect) documentation. To fix this, teams must adopt the Truth-Grounding Protocol (TGP), enforcing verification steps that link AI explanations back to immutable, executable code samples.

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The Problem: The Conceptual Bug Epidemic

The developer workflow is built on trust, especially the trust between code and documentation. When integrating a new service, API, or complex internal component, developers rely on documentation to accurately define inputs, outputs, and side effects.

However, LLMs, when asked to "explain how to use this API," operate by generating text that is statistically likely to be correct and well-written. When dealing with specific, non-public, or niche API parameters, this general knowledge base can produce highly convincing, yet functionally incorrect explanations.

As noted by engineers online, this reliance on AI-generated documentation creates two critical workflow failures:

1. Conceptual Misdirection: The LLM describes the API's behavior in a way that is plausible but wrong. For instance, stating a parameter is required when it's optional, or defining an API endpoint that hasn't existed for three versions.
2. Debugging Blindness: When the resulting code fails, the developer checks the documentation (the AI-generated explanation), sees the code matches the explanation, and incorrectly assumes the problem lies elsewhere (e.g., networking, server-side issues), wasting days on misdiagnosis. The bug is injected by the lie in the documentation.

The output looks like documentation, but it’s just hallucinated context, leading directly to application bugs and substantial technical debt.

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Core Concept: The Truth-Grounding Protocol (TGP)

To restore trust and prevent AI-induced documentation errors from becoming bugs, we introduce the Truth-Grounding Protocol (TGP).

The Truth-Grounding Protocol (TGP): Any AI-generated explanation, documentation, or instruction set must be immediately and automatically grounded by an executable code sample or a reference to an authoritative, unchanging source (e.g., a source code file or OpenAPI schema). Verification occurs when the code sample executes correctly, confirming the AI's explanation.

The TGP ensures that the AI’s verbal dexterity is subservient to the actual executable truth. If an LLM explains an API, the next step in the workflow must be generating a runnable, minimal example based on that explanation, and testing it. If the code fails, the explanation is wrong.

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Step-by-Step Implementation: Enforcing TGP in Workflow

Implementing the TGP means integrating verification steps into the documentation consumption process.

1. Decompose the Explanation into Atomic Facts

Break the AI's prose down into testable, discrete statements about the API.

• AI Explanation Snippet: "The userUpdate(id, data) endpoint requires the data payload to contain an email field of type string and returns the full User object upon success."
• Atomic Facts (to be tested):
  1. email field is required in data. (Test: Send request without email).
  2. email must be a string. (Test: Send request with email: 123).
  3. Success returns a User object. (Test: Check shape of the response).

2. Generate and Execute the Grounding Sample

Use the atomic facts to prompt the AI to generate a minimal, runnable code snippet that tests one fact at a time.

❌ Misleading AI Explanation (Example): "To submit the form, call the asynchronous function submitForm(payload) which automatically handles input validation."

✅ Grounding Sample Prompt (To the AI): "Using the function submitForm, generate a TypeScript test function that attempts to submit a payload missing the required id field. The test must expect an explicit validation error from the function."

3. The Parameter Type Mismatch Example

A common conceptual bug is the parameter type mismatch, which an AI can easily introduce in documentation.

AI-Generated Documentation Claim: "The postData(url, params) function takes params as a simple URL-encoded string."

Actual Code (Internal Implementation): The function actually expects params to be a JavaScript object which it then serializes.

If a developer follows the AI's documentation and writes the code based on the string assumption, the bug is immediately introduced. The TGP demands that the developer check the underlying code or run a test showing the function handles objects, not strings.

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Verification & Testing

Since the bug originates in the mind (the documentation) rather than the code, verification must be behavioral and focused on execution invariants.

1. Contract-Driven Explanation Testing

For internal documentation generated by AI, integrate testing tools that compare the explanation against the source code's contract.

• Tool: Use Jest or Vitest with custom matchers that assert that the generated documentation strings (or markdown blocks) explicitly contain (or do not contain) keywords that violate the type definitions or OpenAPI schema.
• Goal: If the LLM claims status is an integer, the test fails unless the source code confirms status: number (or similar).

2. Boundary Condition Probing

Focus testing on the parameters the AI is most likely to misinterpret: boundary conditions (zero, null, empty string, maximum length) and optional/required status.

• Test: For every parameter the AI describes, run a test case where that parameter is omitted or incorrectly typed and verify the resulting error is correct and expected. This quickly invalidates most AI documentation errors.

3. The "Silent Success" Audit

The most dangerous bug is when the code runs without an error but produces an incorrect result (a silent success).

• Example: The AI doc says a function returns a formatted currency string, but the function actually returns a raw number. The application still runs, but the UI shows $10000 instead of $10,000.00.
• Verification: Add assertion checks to your tests that validate the shape and format of the response object based on the documentation, catching the subtle functional errors introduced by the LLM's imagination.

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Key Considerations & Trade-offs

The decision to use AI for documentation is a calculation between efficiency and informational integrity.

The core trade-off is sacrificing the AI's immediate explanatory output for the reliability of an executable proof. In engineering, accuracy must always precede clarity.

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The greatest threat to reliability isn't knowing how to code; it's believing a false explanation. Don't let AI-generated confidence introduce unfixable conceptual bugs into your architecture.

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