Ship AI to production with cost optimization, observability, streaming, and tool integration.
LLM API costs add up fast in production. Here are the practical strategies that work: prompt caching, model routing, batching, output limits, and cost-per-task tracking.
Quantization shrinks AI models by reducing numerical precision. Here's how it works, what formats exist, and how to choose the right tradeoff between size, speed, and quality.
Traditional monitoring doesn't cover LLM applications. Here's what to log, how to trace multi-step chains, and how to detect quality regressions before users do.
Streaming LLM responses reduces perceived latency and improves UX. Here's how server-sent events work, how to implement streaming with OpenAI and Anthropic, and what to watch for in production.
LLMs generate text, but applications need structured data. Here's how JSON mode, function calling, and schema enforcement turn free-form AI output into reliable, typed data.
MCP standardizes how AI models connect to tools and data. Here's what the Model Context Protocol is, how it works, and why it matters for developers building AI applications.
Get Insanely Good at AI
Chapter 5: Building With AI covers the production trade-offs head-on: cost, latency, accuracy, and when "good enough" is good enough. Real patterns for real products.
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