Industry best practices and patterns
A practical guide to cutting LLM API costs by 60-88% using four optimization layers: semantic caching with Redis, intelligent model routing, provider-level prompt caching, and batch processing with production code examples.
A hands-on guide to building production-grade observability for LLM applications — covering distributed tracing with OpenTelemetry, cost attribution, quality monitoring with LLM-as-judge evaluation, and alerting using Langfuse, Prometheus, and Grafana.
A hands-on guide to building production-grade safety systems for LLM applications. Covers defense-in-depth architecture with NeMo Guardrails, Guardrails AI, OpenAI Guardrails, Llama Guard, and PII detection — with deployable code examples.
A hands-on guide to production prompt engineering in 2026, covering structured outputs for guaranteed schema compliance, prompt chaining, chain-of-thought reasoning, automated optimization with DSPy, and prompt version control with CI testing.
A hands-on guide to building production-grade LLM evaluation pipelines — from DeepEval test suites and custom LLM-as-judge evaluators to golden datasets, GitHub Actions CI/CD integration, and real-time monitoring with Langfuse.