Architecture Patterns for AI-Powered Applications
summary
Walks through concrete architecture choices: a quick email-generator demo that ships in minutes but spikes cost, latency and abuse risk; adding prompt templates then guardrails (NVIDIA NeMo cited) reduces jailbreaks but doubles API calls; adding context enrichment or per-user fine-tuning raises accuracy while exploding cost and model management; recommends RAG + vector DBs for grounded QA, notes hallucinations are intrinsic and proprietary-code leakage from hosted APIs is real. Amadeus example mentioned as applied.
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chapters (9)
- 0:00Introduction and context
- 1:26Defining software architecture
- 10:20AI illities and trade-offs
- 20:16From demo to production
- 32:18Scaling AI in enterprises
- 44:02Semantic search and pipelines
- 50:01AI-ready structured data
- 55:00Advanced RAG patterns
- 1:00:16Closing and key takeaways
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