DEV Community

Neweraofcoding
Neweraofcoding

Posted on

Beyond the "Vibe Check": How to Use the Web Codegen Scorer to Master AI-Generated Code

#webdev #programming #angular #ai

AI code generation is a miracle—until the generated code fails the build, ignores accessibility standards, or uses deprecated APIs. The truth is, code that looks right often isn't production-ready.

That’s why the Angular team at Google created the Web Codegen Scorer: a powerful, open-source tool designed to bring objective, measurable quality checks to the code produced by Large Language Models (LLMs) like Gemini, GPT, and others.

This isn't just a tool for Google; it’s your new secret weapon for trusting and deploying AI-generated code.

🎯 What is the Web Codegen Scorer?

The Web Codegen Scorer is essentially a "fitness test" for generated web code. Instead of relying on manual code reviews to catch AI mistakes, the Scorer runs the code through a gauntlet of automated checks across several critical quality pillars:

  1. Build Success: Does the code compile without errors?
  2. Runtime Integrity: Does the application run without immediate crashes or console errors?
  3. Accessibility (A11y): Does it meet WCAG standards, often integrating tools like the Axe open-source engine?
  4. Security: Does it avoid common vulnerabilities like unsafe innerHTML injection or hardcoded credentials?
  5. Best Practices: Does it adhere to modern framework conventions (e.g., using Angular Signals, standalone components, or modern React hooks)?

The result is a numeric score (out of 100) and a detailed report showing exactly where the LLM succeeded or failed.

🧪 How to Use the Scorer: The Evidence-Based Workflow

The Scorer is not a development dependency; it's an evaluation tool used to optimize your AI interactions. Here is the step-by-step workflow:

Step 1: Installation and Setup

Since the Web Codegen Scorer is open source and typically run as a Node.js package, you can install it globally or within a dedicated evaluation project:

# Install globally via npm
npm install -g web-codegen-scorer

# Setup your environment and API key
# The tool needs access to the LLM you want to test (e.g., Gemini API key)
export GEMINI_API_KEY="YOUR_API_KEY"
Enter fullscreen mode Exit fullscreen mode

Step 2: Define Your Environment and Prompts

The Scorer operates on Environments. An Environment is essentially a set of files that configure the test:

  1. Prompts File: A list of application prompts (e.g., "Create a responsive, accessible To-Do list component," or "Build a multi-step credit card checkout form.").
  2. System Instructions: These are the instructions given to the LLM before the prompt, defining its role and coding style (e.g., "You are an expert Angular developer. Always use Signals and Standalone Components."). This is the primary variable you will iterate on.

You can often use the web-codegen-scorer init command to scaffold the necessary config files.

Step 3: Run the Evaluation

Once your environment is set, you run the main evaluation command. You specify the environment file and the model you wish to test.

# Run the evaluation against a specific environment and model
web-codegen-scorer eval --env=./my-angular-tests.mjs --model=gemini-2.5-flash
Enter fullscreen mode Exit fullscreen mode

This command initiates a powerful process:

  1. For each prompt, it sends the prompt and system instructions to the specified LLM.
  2. The LLM generates the code.
  3. The Scorer attempts to build and run the generated code in a sandboxed environment.
  4. It runs the code through automated checkers (Axe, security raters, etc.).
  5. It also uses an "Auto-Rater" LLM to provide a final score on subjective metrics (like adherence to the prompt).

Step 4: Analyze the Report and Iterate

The most crucial step is reviewing the generated HTML report.

  • The Score: Gives you a quick quantitative measure (e.g., Model A scored 85, Model B scored 72).
  • Diagnostics: Shows detailed breakdowns of failures: build logs, specific accessibility violations, and security warnings. You can often see screenshots of the broken output.

Your Goal: Tweak your System Instructions (the context you feed the LLM) and re-run the evaluation. If you find that adding the instruction, "Ensure all form controls have associated aria-labels and no var declarations," dramatically increases your score, you have successfully optimized your prompt for production quality.

💡 Why This Tool is a Game-Changer

1. End the Vibe Check Era

Before the Scorer, assessing AI code was often based on a "vibe check"—it looked okay, so you ran with it. The Scorer replaces intuition with data, allowing you to prove your code is secure and accessible.

2. Prompt Engineering for Quality

It shifts the focus of prompt engineering from getting the code to generate to getting the code to pass production QA. You can objectively test which set of instructions yields the cleanest, most performant code.

3. Framework Agnostic

While built by the Angular team, the core evaluation logic is framework-agnostic. You can define environments for React, Vue, SolidJS, or even vanilla JavaScript, ensuring consistent code quality across your entire front-end stack.

The Web Codegen Scorer is the necessary bridge between the incredible speed of AI code generation and the stringent quality standards of professional software engineering. Stop guessing, start measuring, and deploy AI-generated code with confidence.

Top comments (0)