Learners will demonstrate mastery by completing a Secure AI Testing Toolkit, where they will evaluate a dependency update, run integration tests, and document their findings, while developing a comprehensive testing suite with pytest that achieves at least 88% coverage. As part of this process, learners will evaluate a sample PR upgrading LangChain from version 0.1.5 to 0.1.8. Working in an off-platform Python environment, they will review changelogs for deprecated features, run security scans to identify vulnerabilities, and perform integration tests to validate compatibility. They will submit a structured report that includes an evaluation of a LangChain upgrade, a testing strategy documentation, and a reflection on the CI/CD pipeline improvements.
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您将学到什么
Evaluate AI code dependencies for security and compatibility, and design testing strategies to ensure long-term code quality and coverage.
您将获得的技能
要了解的详细信息
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该课程共有2个模块
This module introduces learners to secure dependency management practices within modern AI frameworks such as LangChain, LangGraph, and CrewAI. Learners will conduct vulnerability assessments, analyze version updates using semantic versioning (SemVer), and apply software engineering discipline to maintain stable and secure AI environments. The module includes a guided changelog analysis and a dependency upgrade evaluation.
涵盖的内容
2个视频2篇阅读材料2个作业
This module focuses on developing and applying structured testing methodologies for AI and multi-agent systems. Learners will create unit and integration test suites using pytest, design mocks for LLM responses, and achieve measurable code coverage goals. The module blends best practices in test-driven development (TDD) with secure software maintenance principles to ensure reliable AI performance.
涵盖的内容
3个视频1篇阅读材料2个作业1个非评分实验室
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