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Retrieval

Hybrid search, chunking & reranking

Tune chunking, combine keyword and vector search and rerank results for production RAG.

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.

  • AIN 401 CO4

    Evaluate AI systems with golden sets, calibrated LLM-as-judge, cost/latency metrics and CI regression gates, and justify engineering decisions with evidence.

  • MST 501 CO3

    Demonstrate Knowledge, Memory & Retrieval competence: complete the Day 31–45 builds, explain the underlying theory, and defend the phase artefact under live questioning.

  • 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 CO4

    Demonstrate Evaluation, Observability & Retrieval Quality competence: complete the Day 46–60 builds, explain the underlying theory, and defend the phase artefact under live questioning.

  • MST 508 CO3

    Demonstrate Retrieval, RAG & Agents competence: complete the Day 31–45 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