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foundationalAI Literacy
LLM Guardrails, Evals and Agentic MemoryLearn to take LLM applications from prototype to production.
intermediateAI Literacy
Evaluating LLM Output QualityHow to tell whether an LLM feature is actually working.
Matching modules
5 modules- Module 1Why LLM Outputs Break: Understanding Prompt Driftin Evaluating LLM Output Quality · 3 lessonsai-for-engineersai-for-product-managersai-for-data-analystsprompt-engineeringai-evalsquickfoundational
- Module 4Measuring What Matters: Evaluation Metricsin Evaluating LLM Output Quality · 4 lessonsai-for-engineersai-for-product-managersai-for-data-analystsprompt-engineeringai-evalsquickintermediate
- Module 5Putting It Together: A Production-Ready Evaluation Pipelinein Evaluating LLM Output Quality · 3 lessonsai-for-engineersai-for-product-managersai-for-data-analystsprompt-engineeringai-evalsworkflowsquickadvanced
- Module 4Evaluating AI-Generated Personasin Prompt Patterns for Product Discovery · 3 lessonsai-for-product-managersprompt-engineeringai-evalsresponsible-aiquickintermediate
- Module 3How Agents Fail in Productionin What an Agent Actually Is (and What It Cannot Do) · 4 lessonsai-for-product-managersai-for-leadersai-for-foundersagentic-aiai-evalsresponsible-aiquickfoundational