The Production LLM Systems Engineering Course — Build Systems That Last

Production LLM systems engineering is concerned with systems that work reliably for real users over extended time periods, not with achieving state-of-the-art benchmark performance in controlled conditions. Production engineering focuses on: graceful failure handling (what happens when the LLM returns unexpected output), operational observability (how operators monitor and debug the system), maintainability (how the system is updated without breaking existing functionality), and cost efficiency (how the system performs its function with appropriate resource utilisation).

The most applicable software engineering disciplines for production LLM systems are: distributed systems design (for multi-agent architectures coordinated through MCP), API design (for semantic blueprint interfaces and MCP tool schemas), observability engineering (for Glass-Box logging and monitoring), testing and quality assurance (for LLM component testing with mocked responses), and reliability engineering (for failure handling, circuit breakers, and graceful degradation). This course shows how each applies to LLM system engineering.

The most common production LLM system failures are architectural: context management failures (agents receiving irrelevant or overflowing context), coordination failures (agents producing contradictory outputs due to shared context without boundaries), observability failures (inability to diagnose production issues due to black-box architecture), safeguard failures (adversarial inputs bypassing prompt-level safety instructions), and deployment failures (updates breaking existing conversation contexts). The Glass-Box Context Engine architecture addresses each of these systematically.

Software testing for LLM systems requires adapting traditional testing approaches: unit tests mock LLM responses to test non-LLM logic deterministically, integration tests use a controlled test LLM with predictable behavior to test component interactions, golden tests record and replay known good LLM interactions to catch regressions, and property-based tests verify that system invariants hold across a range of LLM response variations. The workshop covers all four testing approaches applied to the Glass-Box Context Engine.

Production LLM system monitoring requires: latency tracking for every component in the processing pipeline, error rate monitoring by failure type and component, quality metric tracking (citation coverage, safeguard trigger rates, output schema conformance), cost tracking for LLM API usage, and capacity monitoring for the supporting infrastructure (vector stores, memory stores, MCP server pools). The Glass-Box logging layer provides the data for all of these metrics, and the workshop covers building monitoring dashboards on top of that data.

The core production LLM systems engineering skills covered in this workshop can be developed in a focused 6-hour session because you implement them hands-on with expert guidance. The broader discipline — understanding trade-offs between different architectural approaches, developing intuition for production failure modes, and gaining experience operating LLM systems at scale — develops over months of practice. This workshop gives you the foundations and the working reference implementation to accelerate that development.