Optimization Tradeoffs User Stories
Notes on Optimization Tradeoffs User Stories for ML platform / Applied AI interview preparation. The file index below shows what's in scope; click through to the individual notes for the depth.
Interview talking points
- Skim the file index below for the questions this folder helps answer.
- Cross-reference notes on related topics from the home page.
Files in this folder
| File | Title |
|---|---|
| README.md | Optimization Tradeoffs User Stories - MangaAssist Chatbot |
| US-01-optimization-trilemma-framework.md | US-01: The Optimization Trilemma — Decision Framework |
| US-02-llm-model-tiering-tradeoffs.md | US-02: LLM Model Tiering — Quality vs Cost vs Latency |
| US-03-latency-budget-allocation.md | US-03: Latency Budget Allocation Across the Pipeline |
| US-04-realtime-vs-precomputed-inference.md | US-04: Real-Time vs Pre-Computed Inference |
| US-05-rag-depth-speed-cost.md | US-05: RAG Retrieval Depth vs Speed vs Cost |
| US-06-cache-aggressiveness-tradeoffs.md | US-06: Cache Aggressiveness — Freshness vs Speed vs Cost |
| US-07-guardrail-strictness-tradeoffs.md | US-07: Guardrail Strictness — Safety vs Latency vs User Experience |
| US-08-autoscaling-cost-vs-performance.md | US-08: Autoscaling Strategy — Cost vs Performance Headroom |
| US-09-token-budget-allocation.md | US-09: Token Budget Allocation — Context Window Partitioning |
| US-10-unified-optimization-dashboard.md | US-10: Unified Optimization Decision Dashboard |
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