8 articles
How to add OpenTelemetry tracing to LLM applications in production — instrumenting Anthropic SDK calls, tracking token usage and latency, connecting LLM traces to your existing observability stack.
How to implement Retrieval Augmented Generation (RAG) for DevOps runbooks and incident knowledge — using ChromaDB as the vector store and Anthropic Claude for answer generation, with real production patterns.
How to implement real-time streaming LLM responses using FastAPI Server-Sent Events (SSE) and the Anthropic SDK — with proper error handling, client reconnection, and production deployment patterns.
How to build LLM agents that use tools to automate DevOps tasks — querying Kubernetes, running Terraform, checking AWS resources — using Claude API's tool use feature with real production patterns.
How to use Claude API's prompt caching feature to dramatically reduce costs and latency for LLM applications with repeated context — system prompts, documents, conversation history, and RAG results.
How to evaluate LLM output quality in production using LLM-as-Judge — building automated evaluation pipelines, scoring rubrics, and golden dataset testing with Claude API. With real code examples.
How to reliably get structured JSON output from Claude API in production — using tool use, Pydantic validation, retry logic, and schema design patterns that prevent the most common failures.
How to manage LLM context windows in production systems — token budgeting, conversation compression, RAG vs context stuffing, and real strategies for keeping your LLM application fast and cheap at scale.