The Reliable AI: Bridging System Engineering and Large Language Models
Large Language Models are incredibly powerful, but fundamentally unpredictable. For engineers tasked with integrating AI into production, this stochastic nature introduces massive risks: spiraling inference costs, silent hallucinations, security vulnerabilities in AI generated code, and unpredictable vendor outages leading to cascading failures.
Drawing from the principles of System Reliability Engineering, this talk explores how to bridge the gap between traditional infrastructure and modern AI. The solution is not better prompting, but better architecture. By wrapping unpredictable AI components in deterministic system patterns, we can tame the frontier and guarantee reliability.
This session will demonstrate practical engineering patterns to measure, monitor, and enforce reliability in AI-powered applications without sacrificing the capabilities of the underlying models.
Key Takeaways
- Endpoint Reliability: How to implement LLM Gateways to survive third-party API outages using circuit breakers, smart retries, and multi-vendor fallbacks.
- Cost Predictability: Strategies to prevent runaway inference bills using architectural caps, semantic caching, smart context compression, advanced RAG, and router patterns that dynamically switch between Dense and Sparse models as well as edge computing.
- Detecting & Recovering from Failures: Moving beyond traditional metrics (latency/error rates) to "Semantic Monitoring" and continuous Evals to automatically catch hallucinations and data drift.
- Security & Sandboxing: Protecting systems from prompt injection and data leaks using Sanitization Middleware and isolated execution environments.
Target Audience
Senior Software Engineers, SREs, and Technical Architects who are building or maintaining AI-integrated applications in production and want to move beyond prototype-level integrations.
