Why LLMs Can’t Validate Themselves

[!IMPORTANT] LLMs can generate code, but they can’t validate it. Vibe-coded workflows need deterministic, explainable, and enforceable static analysis to ensure quality, safety, and governance. Watchful Anvil delivers exactly that.

“Vibe coding is the future. Validation is the safeguard.”

As of 2025, vibe coding—the practice of using large language models (LLMs) to generate software—has fundamentally shifted how automation is developed. While this unlocks unprecedented speed and creativity, it also introduces new risks and validation gaps that LLMs cannot solve on their own.

This page explains why deterministic validation through static analysis—as enabled by Watchful Anvil—is essential in an era where your workflows may be written by prompts, not people.

What Is Vibe Coding?

Coined by Andrej Karpathy in 2025, vibe coding refers to a new paradigm where:

• Developers write prompts, not functions
• LLMs generate plausible, working code
• Humans guide, test, and refine the results

This shifts the developer’s role from manual coding to AI orchestration—but also raises the question: who validates what the model creates?

Why LLM-Generated Code Still Needs Guardrails

1. LLMs generate patterns—not policies

LLMs mimic patterns from training data but lack awareness of your team’s architectural, security, or design rules.

Static analysis enforces what LLMs don’t know—your actual development policies.

2. Generation is probabilistic; validation must be deterministic

LLMs can:

• Skip crucial error handling
• Introduce subtle logic flaws
• Use deprecated or disallowed activities

Validation must be consistent and repeatable—critical for CI/CD, audits, and governance.

3. Your standards are not in the model’s training set

Org-specific rules like:

• “All browser activities must be in a Try/Catch”
• “Workflows must not exceed 3 nested sequences”
• “Queue names must follow enterprise naming conventions”

These rules aren’t public, so LLMs won’t follow them—but static analyzers can enforce them precisely.

4. LLMs hallucinate—analyzers don’t

Language models may generate:

• Non-existent properties or activities
• Improper logic flows
• Broken or invalid syntax

Static analysis applies concrete, testable rules—no hallucination, just precision.

What Makes Static Analysis Essential?

Requirement	Why It Matters
Deterministic	Ensures reproducible validation across devs, machines, and pipelines
Explainable	Surfaces clear reasoning behind rule triggers—enabling trust and education
Enforceable	Can block builds, enforce best practices, and prevent bad code from shipping

How Watchful Anvil Fits In

Watchful Anvil helps teams and contributors:

• Define custom, reusable rules aligned to enterprise needs
• Validate both human- and AI-authored workflows
• Integrate rule checks into source control and CI/CD pipelines
• Serve as a transparent, testable validation layer that scales with automation volume

Summary

LLMs are transforming how we write automation—but they cannot validate themselves.

Watchful Anvil makes deterministic, enforceable validation possible, ensuring that as automation accelerates, quality, safety, and governance remain uncompromised.

“In the age of vibe coding, analyzers aren’t optional—they’re your contract with correctness.”