1.4.2. From PoC to Production
💡 First Principle: A GenAI PoC that works in a demo fails in production for predictable reasons — no error handling, no guardrails, no monitoring, no cost controls, and no evaluation pipeline. The path from PoC to production is architectural, not just scaling.
A PoC validates feasibility: can the FM understand this domain? Does the retrieval system find relevant chunks? Are response quality thresholds achievable? It deliberately skips production concerns to answer these questions quickly.
The PoC-to-production gap for GenAI:
Key additions when moving to production:
- Bedrock Prompt Management — version-controlled, governed prompt templates
- Bedrock Guardrails — content safety at input and output
- CloudWatch + Bedrock Model Invocation Logs — full observability
- Retry logic with exponential backoff — handles API throttling
- Semantic caching — prevents redundant FM calls
- Bedrock Model Evaluations — systematic quality regression testing
⚠️ Exam Trap: The exam frequently presents PoC architectures and asks "what must be added for production?" Always check for: error handling, guardrails, monitoring, prompt governance, and cost controls. The correct answer is almost never just "more compute."
Reflection Question: You're reviewing a teammate's PR to launch a customer-facing chatbot. The code calls bedrock.invoke_model() directly in a Lambda, uses a hardcoded system prompt string in the code, and returns responses without any content filtering. List every architectural concern that would block you from approving this for production.
