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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.
Streaming makes AI features feel responsive instead of frozen. Here's the full pipeline — async views, SSE, JavaScript client, and the nginx tweaks that matter.
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.
Embed documents, store vectors in Postgres, and let an LLM answer questions about your own data — without hallucinating its sources.
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.
Tokens, transformers, context windows, why LLMs hallucinate, and how to choose between Claude, GPT, and open-source models — explained in plain English.
What AI, machine learning, and LLMs actually are, when to use them in your Django app, and a decision framework for picking the right tool.
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