AI Wisdom · Free resource · PDF
The demo worked. Then real users arrived. A 6-page field guide to everything that takes an LLM + RAG system from demo to production, distilled from our featured deep dive.
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What's inside
A one-page diagram of every hop between the user and the model, and what demos skip.
Keyword, SQL, API, hybrid search or direct generation: a routing table with examples.
Hot, warm, cold and archive: stop paying to embed records nobody queries.
Which size of model for which job, with the current OpenAI, Anthropic and Google lineup.
Request path, retrieval, cost, security and launch, with the must-haves marked in red.
The few lines of code that bound every model call, as pseudocode you can port.
Eight signals to watch in production, why each matters and when to page someone.
Seven things that break under real traffic: symptom, likely cause and fix.
Retrieval and generation metrics, scored separately, run in CI on every change.
A week-by-week plan, a "this week" list, and a score that tells you if you are ready.
Every check comes from Building a Production-Ready AI System: the orchestrator, tiered indexing job, token budget gate and telemetry, in C#/.NET on Azure OpenAI and AI Search.
