"What is the SCORE framework for AI coding?"

"SCORE is a framework for responsible AI-assisted development that stands for: Specific Goal (clearly define what you want), Context (tell AI where and what to change), Output Format (specify how you want results delivered), Rules (define constraints, style, and security requirements), and Evaluation (define success criteria and validation methods). This framework helps developers stay in control and accountable when using AI coding tools."

"Why can't I just let AI write all my code?"

"In a real company with real repositories and real users, you cannot just paste AI-generated code without understanding it. You are accountable and responsible for the code that goes into production. AI tools are great at specific tasks, but they cannot take responsibility for the changes. As a professional developer, you must stay on top of changes so you can defend and explain what's happening in your codebase."

"How do I provide context to AI coding tools?"

"Context is the most important part of the SCORE framework. You need to tell AI tools exactly where and what to change, rather than letting them modify random parts of your codebase. Explain which file, which module, and ideally which section needs changes. The more specific context you provide, the more targeted and useful the AI's changes will be."

"What should I include in the evaluation criteria for AI-generated code?"

"Evaluation criteria should include: compilation success (does the code compile?), build success (does npm run build or equivalent work?), linting checks (do code quality checks pass?), test results (do unit and integration tests pass?), and functionality verification (does it solve the original problem?). By defining these evaluation criteria upfront, you can prompt AI to verify its own output and catch errors before they reach production."

"What does the 'Output Format' in SCORE mean?"

"Output Format refers to specifying how you want AI to present its results. You can request: just the diff changes, the full modified code, an explanation with notes, or documentation files (like .md files) that explain what changed and why. Requesting output in specific formats helps you understand, review, and compare changes more effectively before accepting them."

"What are the rules I should set for AI coding tools?"

"Rules include constraints, limits, style guidelines, and security requirements that AI needs to know before writing code. This covers: coding style standards, security requirements (like authentication patterns), architectural constraints, performance requirements, and any project-specific guidelines. Providing rules ensures the AI-generated code meets your project's standards and doesn't introduce security vulnerabilities."

"How do I stay accountable when using AI to write code?"

"Stay accountable by following the SCORE framework: always provide clear goals, give specific context, request appropriate output formats for review, define rules and constraints, and establish evaluation criteria. Never accept AI-generated code without understanding it. You should be able to defend and explain every change. Remember: your job is not to let AI write code, but to stay in control of the change."

Stop Letting AI Write Your Code: The SCORE Framework for Responsible AI Development

Developers are using AI tools like Cursor, Claude Code, GitHub Copilot, and Codex every day. But most are doing it wrong.

Here’s what you need to understand:

Your job is not to let the AI write code. Your job is to stay in control of the change.

The Problem

Yes, AI coding tools are really good now. You can literally say “add authentication” or “fix this error,” and it will do something. It will fix the problem. That’s great.

But here’s the problem:

In a real company with a real repo, with real users, you can’t just paste a bunch of AI-generated code without understanding it.

You cannot just say, “That is the output of the model.”

You are accountable. You are responsible.

So that’s what we’ll talk about in this article: how you stay on top of changes while using AI. I’ll introduce you to the SCORE framework.

Introducing the SCORE Framework

The goal isn’t just to ask AI what you want: it’s also understanding what it does.

So what’s the correct way of doing this? How should you change your mindset?

The SCORE framework helps handle this problem. When talking with AI tools, use these five items:

S - Specific Goal

First, explain what you want. What is your specific goal?

This is the easiest one because you already know what you want.

C - Context

This is the most important part.

Once you explain your problem, you don’t want AI tools to just go and change everything.

If you’re using AI tools regularly, you’ve experienced this. You ask a question and it starts changing 100 random things.

You need to tell AI exactly what to change.

Where is it? You don’t need to pinpoint the exact line, but if you know it, that’s better. At minimum, explain which file and which module.

Give it the context.

O - Output Format

The other important part is output format.

What do you want?

Just see the diff changes?
The full modified code?
Explanatory notes?
Documentation files?

For example, I often ask for a .md file so I can see what’s going on, understand it, and compare changes.

A .md file provides a clear explanation of what the AI did.

R - Rules

Another critical part is rules.

What are your constraints and limits?

Style guidelines
Security requirements
Authentication patterns
Architectural constraints

You’re providing context about what AI needs to know before it starts coding.

Can AI understand without you giving it these details? Yes. But we’re talking about professional development with real codebases. This is a guide for developers who need to maintain control and accountability.

E - Evaluation

Finally, evaluation. This is one of the most important parts.

AI is usually not good at evaluating its own results.

How will it evaluate your code?

You’ve experienced this:

Code that doesn’t compile
npm run build fails
Linting checks don’t pass

These can be your evaluation criteria. Prompt AI to use these techniques to verify its code before presenting it to you.

Final Thoughts

If you’re an experienced developer, you should not just ask AI to do something. You need to stay on top of the changes so you can defend what’s going on.

You cannot just say, “This is what AI did.”

Always:

Provide the goal
Give the context
Specify the output format
Define the rules
Explain how AI will evaluate the result

Thank you guys, see you!

The SCORE Framework Summary

Element	Description	Why It Matters
Specific Goal	Clearly define what you want	Sets clear direction
Context	Tell AI where and what to change	Prevents random changes across codebase
Output Format	Specify how you want results delivered	Helps you understand and review changes
Rules	Define constraints, style, security requirements	Ensures code meets standards
Evaluation	Define success criteria and validation methods	Catches errors before they reach production

Watch the Video

I also shared this framework in video format. You can watch it here:

The Problem#

Introducing the SCORE Framework#

S - Specific Goal#

C - Context#

O - Output Format#

R - Rules#

E - Evaluation#

Final Thoughts#

The SCORE Framework Summary#

Watch the Video#