Prepare for effective use of AI tools

To prepare your services for effective usage of AI tools, you can use the AI Maturity Scorecard template. This Scorecard validates the presence of essential files, pre-commit, safeguards, and observability integrations, ensuring you can effectively leverage AI tools across the software lifecycle.

Create a Scorecard for AI Maturity

Step 1: Create the Scorecard and configure its basic settings

1. On the Scorecards page in your workspace, click Create Scorecard.

2. On the AI Maturity template, click Use.

3. Configure basic settings, including the Scorecard's name, unique identifier, description, and more.

   • Learn about configuring the basic settings in the Creating a Scorecard documentation.

Step 2: Review and modify rules

Cortex's templated rules are based on common industry standards:

AI Maturity: Bronze level rules

• Git PR templates exist git.fileExists("pull_request_template.md") OR git.fileExists("docs/pull_request_template.md") OR git.fileExists(".github/PULL_REQUEST_TEMPLATE.md") OR git.fileExists(".github/pull_request_template.md") OR git.fileExists(".github/PULL_REQUEST_TEMPLATE/*.md") OR git.fileExists("PULL_REQUEST_TEMPLATE/*.md") OR git.fileExists("docs/PULL_REQUEST_TEMPLATE/*.md")

• Pre-commit settings file git.fileExists(".pre-commit-config.yaml")

• Repository contains README file git.fileExists("README.md")

AI Maturity: Silver level rules

• Anthropic Claude instructions in repo git.fileExists("CLAUDE.md")

• Google Gemini instructions in repo git.fileExists("GEMINI.md")

• GitHub Copilot coding agent development environment git.fileExists(".github/workflows/copilot-setup-steps.yml")

• GitHub Copilot instructions in repo git.fileExists(".github/copilot-instructions.md")

• Amazon Q instructions in repo git.fileExists("AmazonQ.md")

AI Maturity: Gold level rules

• Amazon Q rules in repo git.fileExists(".amazonq/rules/*.md")

• GitHub Copilot rules in repo git.fileExists(".github/instructions/*.instructions.md")

• Cursor rules in repo git.fileExists(".cursor/rules/*")

• AI-driven anomaly detection in observability stack datadog.monitors().filter((monitor) => monitor.name.matches(".ai.")).length > 0

You can reorder, delete, and edit rules, add more rules to a level, and assign more points to a rule to signify its importance. Behind each rule is a Cortex Query Language (CQL) query; you can edit the existing CQL or write your own queries to further refine your rules.