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The architecture, evals, monitoring, and process disciplines that take an AI feature from "works in a demo" to "survives real users at scale."
Token economics, model tier selection, prompt caching at scale, and the monitoring you need before AI features eat your budget.
System prompts that scale, structured output, few-shot examples, guardrails, and the patterns that hold up when real users push your AI features.
When your existing Postgres is enough, when to graduate to a dedicated vector DB, and how the major options compare for Django apps.
From pip install to a streaming chat view in production. Authentication, error handling, prompt caching, and cost-aware patterns for Django + Claude.
Reasoning models think before answering. Here's how chain-of-thought prompting works, what Anthropic's extended thinking does differently, and when the extra cost is worth it.
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