Analyze Requirements and Design Patterns
Explore how to analyze business and technical requirements to design effective generative AI architectures on AWS. Understand how to select patterns for latency, cost, and accuracy trade-offs, and learn when to use synchronous, asynchronous, batch inference, or proof-of-concept approaches with Amazon Bedrock.
Effective generative AI architecture does not begin with services, models, or diagrams. It begins with disciplined requirement analysis. In real-world systems, success depends on translating business intent into technical design by deliberately prioritizing constraints.
This skill is critical because exam questions rarely describe an architecture directly. Instead, they present pressure from the business: users demand fast responses, leadership enforces strict budgets, or compliance teams require defensible accuracy. From these signals, candidates are expected to infer which architectural pattern best satisfies the dominant constraint. Treat requirement analysis as a prerequisite, not an afterthought. Only after constraints are clearly identified does the right architecture naturally follow.
Key takeaway: Latency, cost, and accuracy cannot be optimized simultaneously, because improving one will inevitably degrade at least one of the others.
Identifying business and technical constraints in GenAI scenarios
A disciplined approach is to identify the dominant constraint first, then allow that constraint to narrow the design space. For example, a requirement for real-time responses immediately suggests ...