Recent Articles
Production LLM Systems Tutorial 1: End-to-End Application Design
A practical tutorial for designing an end-to-end LLM application with gateway, orchestration, retrieval, tools, inference, caching, telemetry, and failure handling.
Production LLM Systems Tutorial 2: Latency, Cost, and Quality
A practical tutorial on the latency, cost, and quality trade-offs behind model routing, caching, batching, quantization, speculative decoding, and prompt compression.
Production LLM Systems Tutorial 3: Scalable Inference Architecture
A tutorial on scalable LLM inference with vLLM, TensorRT-LLM, SGLang, KV cache management, parallelism, autoscaling, routing, and multi-tenant serving.
Production LLM Systems Tutorial 4: RAG and Data Pipelines
A practical tutorial for building a production RAG pipeline with ingestion, chunking, embeddings, hybrid search, reranking, metadata filters, and index versioning.
Production LLM Systems Tutorial 5: Monitoring and Observability
A tutorial for monitoring LLM applications across system metrics, quality signals, drift, tracing, privacy, and cost attribution.
Production LLM Systems Tutorial 6: Evaluation and A/B Testing
A tutorial for building offline evals, online experiments, regression gates, judge calibration, RAGAS-style metrics, and release workflows for LLM systems.
Production LLM Systems Tutorial 7: Security and Prompt Injection
A practical tutorial for defending LLM systems against direct and indirect prompt injection, data exfiltration, unsafe tool calls, and privilege escalation.
Production LLM Systems Tutorial 8: Human-in-the-Loop Workflows
A tutorial for designing human-in-the-loop LLM workflows with confidence routing, escalation queues, review UX, active learning, and approval gates.
Production LLM Systems Tutorial 9: Cost Optimization
A tutorial on reducing LLM application cost with routing, caching, prompt budgeting, batch processing, quantization, attribution, and token guardrails.
Production LLM Systems Tutorial 10: Versioning and Disaster Recovery
A tutorial for versioning models, prompts, embeddings, retrieval indexes, tools, and policies while designing fallback, rollback, and graceful degradation for LLM systems.