13 Production LLM system design
This chapter covers
- Implementing prompt engineering workflows with version control and testing
- Testing strategies for non-deterministic generative systems
- Deploying safety guardrails and governance frameworks for production
- Adversarial testing and vulnerability assessment for LLM applications
Moving from prototype to production with LLM applications introduces challenges that traditional ML engineering doesn't adequately address. While the fundamentals of robust system design remain essential, generative AI systems demand new approaches to testing, monitoring, and safety that account for their non-deterministic nature.
Now we’ll cover some of the operational discipline required to deploy LLM applications reliably at scale. You'll learn to treat prompts as critical infrastructure requiring version control and systematic testing, implement evaluation frameworks that assess semantic quality rather than exact outputs, and deploy comprehensive safety guardrails that prevent harmful content generation and prompt injection attacks.
The shift from deterministic to probabilistic systems fundamentally changes how we approach quality assurance. Traditional assertion-based testing breaks when the same input produces multiple valid outputs. Instead, you need evaluation frameworks that can assess whether responses are factually correct, appropriately scoped, and aligned with business policies—even when the exact wording varies between runs.