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Last updated: April 29, 2026
APIs, vector databases, chunking strategies, agents — the moment AI goes from toy to production. Includes Slack-bot RAG architecture.
Most AI demos look impressive in a notebook and break instantly in production. This module is about the difference — APIs, retrieval, error handling, monitoring, and the architectural choices that determine whether your AI feature ships or stalls.
The architecture of a production AI app
A production AI app has 6 layers, every single one worth getting right:
- Ingest — pulls your private docs into the system. Files, Notion, Confluence, GitHub, customer tickets.
- Chunk — splits docs into pieces that fit in the LLM’s context. Strategy here makes or breaks RAG.
- Embed — converts each chunk to a vector via an embeddings API.
- Store — saves vectors + chunk metadata in a vector database.
- Retrieve — at query time, embed the user question, find top-K nearest chunks.
- Generate — pass retrieved chunks + question to the LLM, get an answer.
Every layer has failure modes. Most teams obsess over (6) and ignore (2)-(3) — which is why their RAG quality is mediocre.
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