Module 2: Embeddings and semantic similarity

Theme

Embeddings and semantic similarity

Essential Question

How do vector representations support semantic operations?

Module Components

• Book prose: conceptual framing, domain scenario, methods, and failure modes

• Assignment: evidence-backed production of a specific artifact

• Slides: presentation sequence for seminar or lecture delivery

• Narration: spoken version of the slide flow

• Rubric: criteria for evaluating the module artifact

• Notebook: executable lab aligned with the module theme using synthetic support messages, retrieval snippets, intent labels, and factuality checks

Module Artifact

NLP evaluation packet with task framing, retrieval/evaluation design, and deployment guardrails focused on embeddings and semantic similarity: Build a simple semantic search over course documents.

Professional Setting

Students work as if advising a product team evaluating an NLP workflow before using it in customer-facing communication. Their work must be intelligible to product manager, support lead, privacy reviewer, and model evaluator.

Use This Module in Order

1. Read the learning chapter.

2. Review the slide deck with the matching narration.

3. In Populi, open the private student-repository link for this course and enter modules/module-2.

4. Clone the repository once or open its Codespace/Colab copy; run lab.ipynb and complete exercise.ipynb there.

5. Self-check with the rubric, commit and push the work, then submit exactly what Populi requests.