How to evaluate tool-using AI agents: trajectory vs outcome evals, simulated environments, LLM judges, regression gates in CI, with a reference architecture.
A semantic caching architecture for LLM apps: exact vs embedding-similarity cache tiers, thresholds, invalidation, eviction, and the cost/latency math behind GPTCache-class systems.
How to evaluate RAG systems in production: faithfulness, context precision/recall, answer relevancy, RAGAS-style metrics, golden sets, and an evaluation pipeline architecture.
An LLM observability and LLMOps architecture: OpenTelemetry GenAI traces, spans, online evals, and drift detection for production LLM and agent systems.
An LLM gateway architecture for production AI: routing, semantic caching, rate limits, budgets, fallbacks, and observability across multiple model providers.