Courses/AI Engineer/Build Production RAG Systems
AI EngineerIntermediate8 modules~4.0 hours hands-on3 learning modes

Build Production RAG Systems

Build Atlas, an evaluated knowledge assistant with versioned ingestion, hybrid retrieval, reranking, grounded citations, access controls, agentic routing, and production regression gates.

// What you'll be able to do
  • Choose the right retrieval architecture
  • Track, configure, and run Atlas locally
  • Turn changing source files into stable evidence
  • Find the right evidence, not merely similar text
// Curriculum

8 modules, built to run

Every module ships runnable code. Expand any one to see what's inside.

00IntroductionMeet Atlas and the evidence problem30 min
Questions this module answers
  • How do you explain why private changing knowledge needs retrieval?
  • How do you trace evidence from source document to cited answer?
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Questions this module answers
  • How do you choose between no retrieval, long context, search, RAG, and fine-tuning?
  • How do you separate retrieval quality from generation quality?
Questions this module answers
  • How do you initialize a Git-tracked reproducible project?
  • How do you run pinned PostgreSQL with pgvector?
Questions this module answers
  • How do you preserve provenance and stable identities through ingestion?
  • How do you choose chunk boundaries that preserve answerable meaning?
Questions this module answers
  • How do you explain what embeddings preserve and lose?
  • How do you tune top-k and reranking against retrieval metrics?
Questions this module answers
  • How do you assemble bounded context with stable citations?
  • How do you abstain when approved evidence is insufficient?
Questions this module answers
  • How do you treat retrieved content as untrusted data?
  • How do you measure retrieval separately from answer generation?
Questions this module answers
  • How do you run the complete credential-free Atlas flow?
  • How do you ship reproducible tests and CI?

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