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Skill path

Building and Deploying AI Applications

Build an AI feature from prompt and structured output through retrieval, evaluation, production serving, observability, and release.

12 lessons about 13 h of lessons Ends in a mini certificate Paid plans
Lesson checks passed0 of 12
Start: Prompt Engineering: Techniques & Patterns

The route

  1. 1Prompt Engineering: Techniques & PatternsPhase 11: LLM engineering and RAG · 1 h
  2. 2Structured Outputs: JSON, Schema Validation, Constrained DecodingPhase 11: LLM engineering and RAG · 1 h
  3. 3Embeddings & Vector RepresentationsPhase 11: LLM engineering and RAG · 1 h
  4. 4Context Engineering: Windows, Budgets, Memory, and RetrievalPhase 11: LLM engineering and RAG · 1 h
  5. 5RAG (Retrieval-Augmented Generation)Phase 11: LLM engineering and RAG · 1.5 h
  6. 6Evaluation & Testing LLM ApplicationsPhase 11: LLM engineering and RAG · 1.5 h
  7. 7Caching, Rate Limiting & Cost OptimizationPhase 11: LLM engineering and RAG · 1 h
  8. 8Guardrails, Safety & Content FilteringPhase 11: LLM engineering and RAG · 1 h
  9. 9Building a Production LLM ApplicationPhase 11: LLM engineering and RAG · 1.5 h
  10. 10LLM Observability Stack SelectionPhase 17: Infrastructure and production · 1 h
  11. 11Shadow Traffic, Canary Rollout, and Progressive Deployment for LLMsPhase 17: Infrastructure and production · 1 h
  12. 12SRE for AI - Multi-Agent Incident Response, Runbooks, Predictive DetectionPhase 17: Infrastructure and production · 1 h

Building and Deploying AI Applications skill path certificate

Pass all 12 lesson checks on this path (0 done), then the 10-question path quiz drawn from these lessons. The certificate lists every topic you covered and carries an ID anyone can verify.

Quiz requirements