Test and Secure Your AI Code

Overview

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. Throughout the course, learners will engage in hands-on labs, guided coding exercises, in-video questions, interactive dialogues, and scenario-based video quizzes to apply their skills to real-world challenges. The final submission works as a personalized security and testing resource that enables learners to safeguard AI code, improve long-term reliability, and prove readiness to apply critical testing practices in professional AI development environments.

Syllabus

Dependency Management and Security

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.

Comprehensive Testing Strategies

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.