AI Engineering/Phase 11/Lesson 04

Embeddings & Vector Representations

Text is discrete. Math is continuous. Every time you ask an LLM to find "similar" documents, compare meanings, or search beyond keywords, you're relying on a bridge between these two worlds. That bridge is an embedding. If you don't understand embeddings, you don't understand modern AI. You just use it.

Build 1.3 hr Python

Prerequisites: Phase 11, Lesson 01 (Prompt Engineering)

#Learning Objectives

Generate text embeddings using API providers and open-source models, and compute cosine similarity between them
Explain why embeddings solve the vocabulary mismatch problem that keyword search cannot handle
Build a semantic search index that retrieves documents by meaning rather than exact keyword match
Evaluate embedding quality using retrieval benchmarks (precision@k, recall) and choose the right embedding model for your task

Full Access lesson

Keep going. The rest of this lesson is one step away.

Unlock all 503 lessons, video, code, quizzes and every future update. Pay in your own currency with Paystack.

See plans Sign in

The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.