What Reliability Means for LLM Applications
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LLMOps for Reliable AI Applications
A reliability report for an LLM app with eval cases, tests, traces, cost/latency notes, risk register, and release recommendation.
Quality Dimensions: Correctness, Grounding, Format, Safety, Latency, and Cost
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Eval Datasets and Test-Case Design
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Unit Tests for Prompts, Schemas, and Tools
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RAG Evaluation: Retrieval, Citation, and Answer Quality
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Agent Evaluation: Tool Trajectory, Approvals, and Stopping
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Observability: Traces, Metrics, and Logs
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Cost, Latency, Caching, and Rate Limits
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Red-Team and Misuse Cases
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Release Gates and Regression Testing
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Incident Review and Continuous Improvement
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Capstone: Reliability Report for an LLM App
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First-release boundary
This is a text-first course shell. Leonardo/image creation, image QC, dependency-heavy runtime QA, external review, and learner pilot evidence remain separate later quality steps.