{"product_id":"ai-assisted-testing-and-production-monitoring","title":"AI-Assisted Testing and Production Monitoring","description":"\u003cdiv\u003e\n\u003cp\u003eIn this one-day hands-on course, students use AI as a practical engineering assistant across the full software quality lifecycle. Using a continuing case study, students turn a feature request into practical test scenarios, generate test data, create automated test examples, define release-readiness checks, identify production monitoring signals, investigate a simulated incident, and convert production findings into future test improvements.\u003c\/p\u003e\r\n\u003cp\u003eThe course combines AI assistance with proven practices from modern software testing, AI evaluation, observability, and site reliability engineering. Students learn how to use AI to move faster while still relying on risk-based thinking, meaningful evidence, human accountability, quality gates, telemetry, SLOs, incident response, and production feedback.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eAI-Assisted Testing and Production Monitoring Benefits\u003c\/h3\u003e\n\u003cul\u003e\u003cli\u003e\n\u003cp\u003e\u003cb\u003eCourse Benefits\u003c\/b\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eTeams are using AI to create tests, test data, summaries, and automation. But many teams do not have a clear way to check whether the AI’s work is accurate, complete, and safe to use.\u003c\/li\u003e\n\u003cli\u003eMany teams test software before release, but they do not always connect those tests to what actually happens after the system goes live. A system may pass testing but still fail when real users, real data, slow response times, AI tool actions, or weak monitoring are involved.\u003c\/li\u003e\n\u003cli\u003eAI-enabled applications create new testing risks. Traditional tests may not catch problems such as incorrect AI answers, answers that are not supported by source information, attempts to manipulate the AI, exposure of private data, unsafe use of connected tools, changes in AI behavior over time, or failure to hand off risky situations to a person.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003ePrerequisites \u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eAttendees should have basic familiarity with software development concepts, testing concepts, APIs or web applications, CI\/CD concepts, and basic production monitoring concepts. No advanced AI, machine learning, or data science background is required.\u003c\/li\u003e\n\u003cli\u003eHelpful but not required: experience with Postman, REST Client, pytest, Playwright, REST Assured, GitHub, Azure, DevOps tools, or an enterprise AI assistant.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eAI Testing and Monitoring Training Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eLearning Objectives\u003c\/h4\u003e\n\u003cp\u003e\u003cstrong\u003eChapter 1: Build the First Test Set with AI\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eWhere AI fits in the testing workflow\u003c\/li\u003e\n\u003cli\u003eHow to give AI enough context to produce useful test work\u003c\/li\u003e\n\u003cli\u003eMoving from feature request to test scenarios\u003c\/li\u003e\n\u003cli\u003eNormal paths, boundary paths, edge cases, and negative paths\u003c\/li\u003e\n\u003cli\u003eExpected results and test intent\u003c\/li\u003e\n\u003cli\u003eKeeping the engineer responsible for final test decisions\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eHands-On Exercise 1: Use AI to interpret a support-assistant feature request, generate test scenarios, create normal, boundary, edge-case, and negative tests, add expected results, and organize the tests into a usable test set.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eChapter 2: Generate Test Data and Automated Test Examples\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eGenerating realistic test data\u003c\/li\u003e\n\u003cli\u003eCreating boundary and edge-case data\u003c\/li\u003e\n\u003cli\u003eCreating negative and invalid data\u003c\/li\u003e\n\u003cli\u003eAvoiding unsafe or unrealistic synthetic data\u003c\/li\u003e\n\u003cli\u003eGenerating API or functional test examples\u003c\/li\u003e\n\u003cli\u003eAdding assertions\u003c\/li\u003e\n\u003cli\u003eMaking tests readable and maintainable\u003c\/li\u003e\n\u003cli\u003eUsing AI to explain test failures and suggest next steps\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eHands-On Exercise 2: Use AI to generate JSON test data, create API or functional test examples, add assertions and expected outcomes, run or inspect the test logic, and improve the test set so it would be useful to a real team.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eChapter 3: Test AI-Enabled Behavior\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eWhy AI-enabled features need additional evaluation\u003c\/li\u003e\n\u003cli\u003eTesting prompts and expected behavior\u003c\/li\u003e\n\u003cli\u003eTesting retrieved context and grounded answers\u003c\/li\u003e\n\u003cli\u003eTesting tool-call selection and tool-call arguments\u003c\/li\u003e\n\u003cli\u003eTesting uncertain or unsupported answers\u003c\/li\u003e\n\u003cli\u003eChecking for hallucination, poor grounding, and unsafe responses\u003c\/li\u003e\n\u003cli\u003eTesting prompt injection and data exposure risks\u003c\/li\u003e\n\u003cli\u003eCreating simple evaluation checks for AI behavior\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eHands-On Exercise 3: Use AI to create evaluation examples for the support assistant, define expected answer qualities, create checks for grounded responses, create checks for tool-call behavior, and create checks for refusal, escalation, or uncertainty handling.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eChapter 4: Prepare the Release with AI-Assisted Quality Gates\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eWhy passing tests alone is not enough\u003c\/li\u003e\n\u003cli\u003eRelease confidence as a body of evidence\u003c\/li\u003e\n\u003cli\u003eQuality gates for traditional application behavior\u003c\/li\u003e\n\u003cli\u003eQuality gates for AI-enabled behavior\u003c\/li\u003e\n\u003cli\u003eRegression risk and change risk\u003c\/li\u003e\n\u003cli\u003ePrompt injection, data leakage, weak tool controls, and unsafe automation behavior\u003c\/li\u003e\n\u003cli\u003eCanary releases, feature flags, and rollback planning\u003c\/li\u003e\n\u003cli\u003eMonitoring readiness before release\u003c\/li\u003e\n\u003cli\u003eAI-assisted release summaries\u003c\/li\u003e\n\u003cli\u003eHuman approval and accountability\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eHands-On Exercise 4: Use AI to create a release-readiness checklist, define quality-gate criteria, identify required release evidence, prepare a deployment risk summary, and decide what monitoring must be in place before production release.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eChapter 5: Monitor Production and Investigate an Incident with AI\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eMonitoring known failures versus investigating unknown behavior\u003c\/li\u003e\n\u003cli\u003eLogs, metrics, traces, events, dashboards, and alerts\u003c\/li\u003e\n\u003cli\u003eSLOs, SLIs, and error budgets\u003c\/li\u003e\n\u003cli\u003eMonitoring AI-enabled applications\u003c\/li\u003e\n\u003cli\u003eLatency, cost, token usage, failed responses, grounding, tool calls, and user feedback\u003c\/li\u003e\n\u003cli\u003eModel drift, data drift, prompt drift, and evaluation drift\u003c\/li\u003e\n\u003cli\u003eUsing AI to summarize telemetry\u003c\/li\u003e\n\u003cli\u003eUsing AI to suggest likely causes\u003c\/li\u003e\n\u003cli\u003eValidating AI-assisted incident findings\u003c\/li\u003e\n\u003cli\u003eTurning incidents into future tests and release improvements\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eHands-On Exercise 5: Use AI to review simulated production telemetry, summarize symptoms from logs, metrics, and traces, identify likely causes, recommend immediate response actions, create follow-up test cases based on the incident, and improve the release gate or monitoring 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