Analyze Requirements and Design Patterns
Explore disciplined techniques to analyze business requirements and prioritize constraints such as latency, cost, and accuracy for generative AI. Understand how to map these needs to AWS architectural patterns including synchronous inference with Amazon Bedrock, asynchronous workflows, batch processing, and proof-of-concept designs. Learn to select and optimize patterns based on dominant constraints to build effective and scalable GenAI solutions.
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 ...