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Building and Deploying AI Applications
TopicBuild an AI feature from prompt and structured output through retrieval, evaluation, production serving, observability, and release.
~13h12 lessons
Lessons
- Prompt Engineering: Techniques & Patterns~1h
- Structured Outputs: JSON, Schema Validation, Constrained Decoding~1h
- Embeddings & Vector Representations~1h
- Context Engineering: Windows, Budgets, Memory, and Retrieval~1h
- RAG (Retrieval-Augmented Generation)~1h
- Evaluation & Testing LLM Applications~1h
- Caching, Rate Limiting & Cost Optimization~1h
- Guardrails, Safety & Content Filtering~1h
- Building a Production LLM Application~2h
- LLM Observability Stack Selection~1h
- Shadow Traffic, Canary Rollout, and Progressive Deployment for LLMs~1h
- SRE for AI — Multi-Agent Incident Response, Runbooks, Predictive Detection~1h