Production-tested LLM cost optimization tactics. Prompt caching, model routing, semantic caching, batching, fine-tuning small models, output bounds, and the architecture decisions that make the cost line item bearable.
Reliable structured output from LLMs in 2026. Pydantic AI, Instructor, OpenAI’s structured outputs / Anthropic tool calling, retry-on-validation patterns, and the end of homemade JSON parsing.
Why AI gateways became standard infrastructure in 2026. The OpenAI-compatible façade pattern, LiteLLM vs Portkey vs Helicone vs OpenRouter, fallbacks, caching, observability, cost control, and how to drop one in front of an existing app.
When multi-agent systems beat single-agent, the supervisor / worker / reviewer / critic patterns, hierarchical vs swarm, communication models, evaluation, and the production patterns that survive contact with users.
A practical 2026 decision guide for LLM teams. When fine-tuning earns its cost, when RAG is right, when prompting is enough, the hybrid patterns, and the ops realities that change which one fits.
MCP from first principles — the protocol, transports (stdio, HTTP), tools/resources/prompts, why it works, and a concrete walkthrough of building an MCP server in Python and TypeScript.
A practical guide to self-hosting LLMs in 2026. Ollama for development, vLLM and SGLang for production, model selection (Llama 3.3, Qwen 2.5, DeepSeek V3), hardware sizing, batching, and when self-hosting is genuinely cheaper than the API.
A practical guide to LLM evaluations — what to measure, building eval sets, LLM-as-judge done right, RAG-specific metrics, and integrating evals into CI so you stop shipping silent regressions.
The prompt patterns I keep reaching for in production LLM apps — system prompt structure, role separation, structured output, few-shot, chain-of-thought, prompt caching, and the anti-patterns to skip.
How to actually use the Anthropic Claude API in production. Messages format, tool use, prompt caching for 90% cost cuts, structured outputs, streaming, and the gotchas worth knowing.