Embeddings, RAG and Semantic Search

This course covers OpenAI embeddings, vector databases, semantic search, a complete RAG pipeline, and production applications.

22 lessons About 2 h 15 min Developer

What you'll learn

Generate embeddings and measure semantic proximity between texts using cosine similarity
Index a corpus in a vector database and set up hybrid semantic and lexical search
Build a complete RAG pipeline: chunking, retrieval, reranking, then generation
Evaluate the relevance of a RAG pipeline and choose between File Search and a custom architecture
Use embeddings to classify, cluster, recommend, and detect anomalies and duplicates

Who it's for: Developers, engineers, and integrators who want to use ChatGPT via API in their applications.
Prerequisites: Ability to read Python and JSON, command-line basics. An OpenAI platform API key to try the examples.
Length: 2 h 15 min · 22 lessons
Format: 100% online self-paced course, accessible 24/7 from a browser. Progress is saved, and you can resume at any time. Estimated duration: 2 h 15 min.
Assessment: Self-assessment based on the Key takeaways that conclude each lesson, and direct practice of the examples on your own examples. A final 5-question quiz with answers, at the end of the last lesson, validates what you have learned.
Accessibility: Self-paced course accessible to people with disabilities: text content compatible with screen readers, keyboard navigation, and compliant color contrast. For any adaptation needs, email [email protected].
Price: Free
Recognition: Certificate of completion issued by Corsen AI, verifiable online.
Syllabus: Download the syllabus (PDF)