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Free RAG and Vector Databases Course

Master Retrieval Augmented Generation and vector databases in this free course. Learn to build accurate AI systems using LangChain, semantic search, embedding models, and advanced retrieval strategies from scratch to production-ready applications.

10 lessons 14h 19min total Free

Course overview

This course delivers a comprehensive, hands-on journey into Retrieval Augmented Generation (RAG) and the vector databases that power it. You will move from foundational concepts to advanced implementations, learning how to ground large language models with real-time, external data. The curriculum covers the complete RAG pipeline: indexing, retrieval, and generation. You will explore critical techniques like document chunking, semantic similarity search, query expansion, and multi-hop reasoning. Through step-by-step tutorials, you will work with core technologies including LangChain, FAISS, MongoDB Atlas, and various embedding models to build systems that produce accurate, factual, and contextually relevant outputs.

Who this course is for

This course is ideal for software developers, data scientists, and AI practitioners who want to move beyond standalone large language models. It serves learners who need to build AI applications that can access proprietary, up-to-date, or domain-specific knowledge without retraining. Whether you are a beginner seeking a clear conceptual foundation or an experienced engineer looking to implement production RAG systems, the content scales. You will find value if you work with customer support bots, internal document search, or any system requiring an LLM to provide grounded, verifiable answers from external data sources.

How to study this sequence

Begin with the foundational lessons on embeddings and vector databases to build a solid conceptual base before moving to the core RAG architecture tutorials. Follow the sequence to first understand the three-stage pipeline, then implement simple systems, and finally tackle advanced optimization patterns like query decomposition and HyDE. For the hands-on tutorials, code along with the provided Python examples using LangChain and vector stores like FAISS. Once you complete the full pipeline, revisit the advanced techniques lesson to refine your system with fallback strategies, reranking, and agentic approaches for robust, production-ready performance.

What you should be able to do

Upon completing this course, you will be able to design and implement a complete Retrieval Augmented Generation system from scratch. You will have practical experience building semantic search functionality, integrating vector databases, and optimizing retrieval pipelines to reduce hallucinations. You will understand how to select appropriate embedding models and chunking strategies for different document types. The final result is the ability to build an AI application that can intelligently chat with your own document collections, providing accurate, source-grounded answers by combining the generative power of LLMs with your specific data.

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What you learn in this RAG and Vector Databases course

This course organizes 10 RAG and Vector Databases lessons in a practical order, starting with the base and moving toward examples you can repeat in your own projects.

The main practice points are Implement the three-stage RAG pipeline: indexing, retrieval, and generation, Apply advanced query translation techniques including Multi-Query, RAG Fusion, and Decomposition, Build intelligent routing and query construction systems for dynamic data access and Optimize indexing strategies using multi-representation and hierarchical approaches like RAPTOR. Use this page to review the course proposal, check the key topics, and open the lessons in the recommended order.

What you will practice

  • Implement the three-stage RAG pipeline: indexing, retrieval, and generation
  • Apply advanced query translation techniques including Multi-Query, RAG Fusion, and Decomposition
  • Build intelligent routing and query construction systems for dynamic data access
  • Optimize indexing strategies using multi-representation and hierarchical approaches like RAPTOR
  • Design adaptive RAG systems with corrective mechanisms and fallback strategies
  • Understand how embeddings represent text semantically in vector space

Concepts covered

Retrieval Augmented Generation Vector Embeddings Semantic Similarity Query Decomposition Hypothetical Document Embeddings Recursive Abstractive Processing Late Interaction Retrieval Embeddings

Course lessons

10 tutoriais organizados em sequência.

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Course summary

This RAG and Vector Databases course brings together 10 lessons in a guided sequence. It starts with "Learn RAG From Scratch – Python AI Tutorial from a LangChain Engineer" and moves step by step, so each lesson supports the next one and helps turn the topic into practice.

During the course, the practical focus is on Implement the three-stage RAG pipeline: indexing, retrieval, and generation, Apply advanced query translation techniques including Multi-Query, RAG Fusion, and Decomposition, Build intelligent routing and query construction systems for dynamic data access and Optimize indexing strategies using multi-representation and hierarchical approaches like RAPTOR. These points help you watch with a goal instead of treating the lesson as a loose introduction.

The course also introduces important ideas such as Retrieval Augmented Generation, Vector Embeddings, Semantic Similarity, Query Decomposition and Hypothetical Document Embeddings, which makes it easier to understand later tutorials and decide when each resource is useful.

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How to get more from this course

Who this RAG and Vector Databases course is for

This advanced course is for learners who already understand the fundamentals of RAG and Vector Databases and want to develop deeper technical or professional skills.

What you can do after finishing

By the end, you should be able to review the main idea of the course and practice Implement the three-stage RAG pipeline: indexing, retrieval, and generation, Apply advanced query translation techniques including Multi-Query, RAG Fusion, and Decomposition, Build intelligent routing and query construction systems for dynamic data access and Optimize indexing strategies using multi-representation and hierarchical approaches like RAPTOR with more confidence.

Recommended study order

Watch the 10 lessons in the order shown on this page. Pause after each lesson to repeat the examples and only move on when the previous step makes sense.

What to study next

After finishing the course, explore the RAG and Vector Databases topic to find related tutorials and keep studying with more context.

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Course questions

Is this RAG and Vector Databases course for beginners?

No. This course covers advanced RAG and Vector Databases topics and is best suited to learners who already understand the fundamentals.

How many lessons are in this course?

This course currently has 10 lessons and about 14h 19min in total.

What should I do after finishing the course?

The best next step is to explore the RAG and Vector Databases topic and choose a related tutorial that matches what you want to practice.

Is this course free?

Yes. This is a free course organized by CursoB so you can study RAG and Vector Databases in a structured sequence.

Do I need previous experience to follow the course?

Yes. You should already understand the fundamentals of RAG and Vector Databases before starting this advanced course.

Should I watch the lessons in order?

Yes. The recommended path is to watch the 10 lessons in the order shown, because each lesson helps prepare the next one.

Can I use this course for practical projects?

Yes. Use the course as a practical reference to train Implement the three-stage RAG pipeline: indexing, retrieval, and generation, Apply advanced query translation techniques including Multi-Query, RAG Fusion, and Decomposition, Build intelligent routing and query construction systems for dynamic data access and Optimize indexing strategies using multi-representation and hierarchical approaches like RAPTOR and adapt the examples to your own projects.

Does this course include a certificate?

CursoB stopped issuing certificates for its own courses in May 2026. CursoB no longer offers completion certificates. Courses and tutorials remain free to study. If content is hosted on another platform, any certificate depends exclusively on the original author or platform rules.

Are the lessons updated?

CursoB periodically reviews the RAG and Vector Databases catalog and may add, remove, or reorder lessons when better content is found.

Where can I find more content on this subject?

Explore the RAG and Vector Databases topic to find related tutorials and complementary courses.

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What each course lesson covers

Use these notes to understand the sequence before opening each lesson.

  1. 01 Learn RAG From Scratch – Python AI Tutorial from a LangChain Engineer In this lesson, you practice Implement the three-stage RAG pipeline: indexing, retrieval, and generation, Apply advanced query translation techniques including Multi-Query, RAG Fusion, and Decomposition and Build intelligent routing and query construction systems for dynamic data access.
  2. 02 OpenAI Embeddings and Vector Databases Crash Course In this lesson, you practice Understand how embeddings represent text semantically in vector space, Create embeddings using OpenAI's API with Postman and Store and index vectors efficiently in SingleStore vector database.
  3. 03 What is a Vector Database? Powering Semantic Search & AI Applications In this lesson, you practice Understand how vector embeddings represent semantic meaning in data, Explore semantic search techniques and their advantages over keyword matching and Learn retrieval-augmented generation (RAG) and its role in accurate AI systems.
  4. 04 RAG Explained For Beginners In this lesson, you practice Understand the three-step RAG process and why it revolutionizes AI document search, Implement vector embeddings and semantic search for intelligent document retrieval and Apply chunking strategies tailored to different document types and structures.
  5. 05 RAG Explained in 12 Minutes In this lesson, you practice Understand the foundational concepts of RAG and how it differs from standard language model approaches, Debunk common myths about RAG obsolescence and the limitations of large context windows and Design effective document chunking strategies and RAG system architecture.
  6. 06 Vector Search RAG Tutorial – Combine Your Data with LLMs with Advanced Search In this lesson, you practice Understand how vector embeddings represent data semantically, Implement vector search in MongoDB Atlas for semantic similarity queries and Build retrieval-augmented generation systems with LangChain and OpenAI.
  7. 07 Complete RAG Crash Course With Langchain In 2 Hours In this lesson, you practice Understand the fundamental principles and architecture of Retrieval-Augmented Generation systems, Implement end-to-end RAG pipelines using LangChain framework and its modular components and Integrate vector databases and embedding models for efficient document retrieval.
  8. 08 What is RAG ? | Completely Explained in 15 Minutes In this lesson, you practice Understand the core principles of Retrieval-Augmented Generation and how it differs from traditional LLMs, Implement the complete RAG pipeline including embedding, retrieval, and generation stages and Evaluate different RAG architectures and select appropriate designs for specific use cases.
  9. 09 Local Retrieval Augmented Generation (RAG) from Scratch (step by step tutorial) In this lesson, you practice Build a complete RAG pipeline from scratch without high-level frameworks, Extract and preprocess PDF documents for information retrieval and Create embeddings and perform semantic search using vector databases.
  10. 10 RAG Fundamentals and Advanced Techniques – Full Course In this lesson, you practice Understand the core architecture and components of Retrieval-Augmented Generation systems, Build a complete RAG application capable of intelligently chatting with document collections and Identify and overcome the limitations and pitfalls of naive RAG implementations.