Embeddings, RAG, and Semantic Search

Build semantic search systems: text and code embeddings, RAG from scratch with FAISS, and advanced techniques (Corrective, Self-RAG, Adaptive).

18 lessons About 2 h Developer

What you'll learn

Generate embeddings with mistral-embed and measure semantic similarity between two texts
Split and batch a large corpus to vectorize it without exceeding API limits
Index code with codestral-embed to enable semantic search in a repository
Build a complete RAG pipeline: preparation, vectorization, FAISS storage, retrieval, and generation
Compare Corrective RAG, Self-RAG, and Adaptive RAG to improve response relevance

Who it's for: Developers, engineers, and integrators looking to leverage Mistral AI via API in their applications.
Prerequisites: Ability to read Python and JSON, command line basics. A Mistral AI Studio API key to try the examples.
Length: 2 h · 18 lessons
Format: 100% online self-paced course, accessible 24/7 from a browser. Progress saved, resume anytime. Estimated duration: 2 h.
Assessment: Self-assessment based on the Key takeaways concluding each lesson, and direct implementation of examples on your own examples. A final quiz of 5 corrected questions, at the end of the last lesson, validates acquired skills.
Accessibility: Course accessible to people with disabilities: text content compatible with screen readers, keyboard navigation, and compliant contrasts. For any adaptation needs, email [email protected].
Price: Free
Recognition: Certificate of completion issued by Corsen AI, verifiable online.
Syllabus: Download the syllabus (PDF)