ASign in
← All skills

Retrieval

Embeddings & vector search

Create embeddings and query vector stores such as pgvector or Qdrant.

Target proficiency: Applied

Course outcomes

  • AIN 401 CO2

    Apply prompt, context and retrieval (RAG) engineering, including structured outputs and tool calling, to build LLM features on a cloud AI platform or open-weight stack.

  • MST 505 CO2

    Demonstrate Retrieval & Knowledge Context competence: complete the Day 16–30 builds, explain the underlying theory, and defend the phase artefact under live questioning.

  • MST 506 CO1

    Demonstrate Foundations — Data, Retrieval Theory & the Corpus competence: complete the Day 1–15 builds, explain the underlying theory, and defend the phase artefact under live questioning.

  • MST 506 CO2

    Demonstrate Vector Databases & Retrieval Engineering competence: complete the Day 16–30 builds, explain the underlying theory, and defend the phase artefact under live questioning.

  • MST 506 CO6

    Demonstrate Agentic Retrieval, Scale & Mastery competence: complete the Day 76–90 builds, explain the underlying theory, and defend the phase artefact under live questioning.

  • MST 509 CO4

    Demonstrate Semantic Access & Context Engine competence: complete the Day 46–60 builds, explain the underlying theory, and defend the phase artefact under live questioning.

Taught in weeks & phases

Textbook chapters