Overview
How to ship reliable retrieval-augmented LLM systems beyond the demo stage. This piece breaks down practical decisions, real-world constraints and the architecture choices that made it work at scale — drawn from hands-on delivery across production systems.
We'll look at the trade-offs between speed and reliability, how to keep cost under control, and the operational patterns that keep these systems healthy long after launch.
The approach
How to ship reliable retrieval-augmented LLM systems beyond the demo stage. This piece breaks down practical decisions, real-world constraints and the architecture choices that made it work at scale — drawn from hands-on delivery across production systems.
We'll look at the trade-offs between speed and reliability, how to keep cost under control, and the operational patterns that keep these systems healthy long after launch.
Architecture & trade-offs
How to ship reliable retrieval-augmented LLM systems beyond the demo stage. This piece breaks down practical decisions, real-world constraints and the architecture choices that made it work at scale — drawn from hands-on delivery across production systems.
We'll look at the trade-offs between speed and reliability, how to keep cost under control, and the operational patterns that keep these systems healthy long after launch.
Key takeaways
How to ship reliable retrieval-augmented LLM systems beyond the demo stage. This piece breaks down practical decisions, real-world constraints and the architecture choices that made it work at scale — drawn from hands-on delivery across production systems.
We'll look at the trade-offs between speed and reliability, how to keep cost under control, and the operational patterns that keep these systems healthy long after launch.
Technical Head • AI & Agentic Systems Architect • Enterprise Solutions Leader