Gwen Davis shares key strategies for maintaining high code quality while leveraging AI tools like GitHub Copilot and Code Quality, helping developers balance speed with reliability.

Balancing Speed and Quality with AI and GitHub Copilot in Development

Author: Gwen Davis

AI technologies such as GitHub Copilot have transformed modern development workflows by dramatically increasing coding speed. However, without the right guardrails, this acceleration can lead to unreliable code and technical debt.

The Challenge: Speed vs. Control

Developers now use AI to automate coding tasks, rapidly building features and applications.
“AI slop” refers to code generated quickly by AI that may look functional but hides bugs, poor imports, or duplicated logic.
True effectiveness comes from pairing velocity with rigorous code quality controls.

Strategy 1: Use AI-Powered Code Analysis

GitHub Code Quality (powered by CodeQL and LLM-based analysis) actively identifies maintainability problems as you code.
Enables:
- Single-click activation for repository-wide analysis.
- Automatic suggestions and fixes on pull requests, such as eliminating unused variables and duplicated logic.
- Example fix:
```
export function calculateFuelUsage(laps, fuelPerLap) {
  if (!Array.isArray(laps) || typeof fuelPerLap !== "number") {
    throw new Error("Invalid input");
  }
  return laps.length * fuelPerLap;
}
```
- Rulesets block merges that do not meet quality standards.
- AI Findings page surfaces actionable legacy technical debt.

Strategy 2: Guide Your AI Tools With Clear Intent

Quality depends on how well you communicate objectives and constraints to AI tools.
Prompting tips:
- Set explicit goals (e.g., “Improve readability and maintainability; no breaking changes”).
- Define constraints (“No third-party dependencies”, “Must remain compatible with v1.7”).
- Reference related files, context, or documentation.
- Specify output formats (pull request, diff, code block).
Multi-step prompts are supported by the GitHub Copilot coding agent, allowing developers to delegate complex tasks with specific requirements.

Strategy 3: Document Your Technical Reasoning

Effective developers go beyond code—sharing why decisions are made, evaluating trade-offs, and narrating rationale.
Best Practices:
- Create issues that frame the technical problem and success criteria.
- Use meaningful branch names and instructive commit messages.
- Annotate alternatives and thought process in documentation, pull request descriptions, and comments:
```
- Added dark mode toggle to improve accessibility and user preference support.
- Chose localStorage for persistence to avoid server dependency.
- Limited styling changes to existing themes to reduce risk.
```

Moving Forward

Pairing AI-driven velocity with robust quality systems and visible thinking enables teams to deliver reliable software quickly and sustainably. Make use of GitHub Copilot, Code Quality, and clear technical documentation to foster control amid accelerated development.

Links:

This post appeared first on “The GitHub Blog”. Read the entire article here