Intermediatecourse · Free
Building with AI Systems
Understand language models, retrieval, agents, evaluation, cost, and deployment safeguards through practical system design.
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Outcomes
What you will be able to do
- Choose an architecture based on task and evidence needs.
- Evaluate quality, latency, cost, and safety together.
- Design monitoring and incident response before launch.
Recommended first: AI Foundations, Responsible AI User
Curriculum
Course modules
Language-model systems
Outcome: Reason about tokens, context, generation, and model tradeoffs.
Practice: Compare two model options using quality, latency, context, privacy, and cost.
Language-model mechanicsModels, inference, and limitationsModel cost and operational tradeoffsGrounding with retrieval
Outcome: Know when and how retrieval can improve evidence access.
Practice: Design a retrieval test set with answer and citation requirements.
Alma-artırılmış nesilSource and claim verificationAgents and tools
Outcome: Bound multi-step systems with permissions and checkpoints.
Practice: Write an agent permission model and failure-recovery path.
Agents, tools, and long-running tasksAutomation boundaries and safeguardsAI security and misuse riskEvaluation and operations
Outcome: Measure system performance before and after deployment.
Practice: Create an evaluation suite covering quality, refusal, latency, cost, and regressions.
Evaluation and benchmark literacySuccess metrics and monitoringAI incident responseExperiment and pilot design
Applied capstone
AI system design review
Produce an architecture and evaluation plan for a source-grounded AI application.
- Architecture diagram
- Evaluation dataset
- Cost and latency budget
- Security, monitoring, and rollback plan