What you'll learn

Imagine searching for “cozy Italian place with outdoor seating” and getting perfect restaurant recommendations—even though no review contains those exact words. Traditional keyword search can’t do this, but embeddings can.

In this course, OpenAI Embeddings API, you’ll gain the ability to build an intelligent restaurant finder powered by semantic understanding.

First, you’ll explore how embeddings represent text as numerical vectors and why they dramatically outperform keyword-based approaches.

Next, you’ll discover how to use OpenAI’s latest embedding models to generate and compare embeddings from real Yelp restaurant reviews using Python notebooks.

Then, you’ll build a full semantic search system and integrate it with a Pinecone vector database for production-scale retrieval.

Finally, you’ll learn how to build a retrieval-augmented generation (RAG) system that can answer natural language questions about restaurants, cluster dining experiences, and deploy at scale.

When you’re finished with this course, you’ll have the skills and knowledge of text embeddings, vector databases, and semantic search needed to build intelligent, context-aware applications that understand your users’ intent.