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Design robust decision systems
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Module 6
AI Economics & Cost Optimization
LLM Inference Costs: Tokens & Context Window
Explain how token-based pricing and context window size impact LLM inference costs.
2
Estimating AI Inference Costs
Calculate the monthly inference cost for an AI feature given user base, usage patterns, and token count.
3
Model Tiering: Balancing Cost and Performance
Explain model tiering as a strategy for balancing cost and performance.
4
Quantifying AI Hallucination Costs
Define 'hallucination cost' by estimating the potential business impact of an AI making a critical error.
5
Optimizing AI Costs: Caching & Deduplication
Explain how response caching and request deduplication can reduce AI operational costs.
6
Batch vs. Real-Time Inference: Cost-Performance Trade-offs
Describe batch inference and when it is preferable to real-time processing for cost-performance trade-offs.
7
Quantifying AI Cost Savings Through Caching & Batching
Recalculate AI feature costs after applying a caching or batching strategy to quantify savings.
8
Optimizing AI Feature Break-Even: A Timeline Recalculation
Recalculate the break-even timeline for an AI feature after applying cost optimization strategies.
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Architecting Agentic Systems
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Designing for Data Flow & Freshness