This course is part of the Generative AI and LLMs ExpertTrack
Retrieval-Augmented Generation and Vector Databases
Learn retrieval-augmented generation and build knowledge-grounded AI applications using advanced RAG techniques.
Duration
3 weeksWeekly study
3 hours100% online
How it worksIncluded in an ExpertTrack
Course 3 of 4
Retrieval-Augmented Generation and Vector Databases
Build retrieval-augmented generation systems
Large language models can generate powerful responses, but they’re limited by the information they were trained on. Retrieval augmented generation (RAG) helps overcome this by connecting LLMs to external sources of knowledge.
On this three-week course, you’ll learn how RAG systems work and develop practical skills to build, customise, and evaluate them. You’ll explore text embeddings, semantic similarity, and vector search before working with vector databases including Chroma, Pinecone, and Weaviate.
Work with vector databases and semantic search
Discover how vector databases store and retrieve information based on semantic meaning rather than simple keyword matching. You’ll learn how to create indexes, manage metadata, perform CRUD operations, and implement similarity searches.
You’ll also explore different embedding models and vector database technologies. By comparing different approaches, you’ll develop the knowledge to select the right tools for your RAG applications.
Build and evaluate advanced RAG applications
Learn how to build complete RAG pipelines using retriever, augmenter, and generator components. You’ll work with document loaders and retrieval chains, customise prompts, and explore advanced techniques such as re-ranking, query transformation, hybrid search, and self-querying retrievers.
You’ll also learn how to evaluate retrieval and generation quality, diagnose weaknesses, and improve your pipelines. The course culminates in a hands-on project where you’ll build a document-based knowledge assistant capable of producing accurate, grounded answers.
By the end, you’ll be able to design, build, and evaluate RAG systems, preparing you for roles involving LLM application development and retrieval-augmented generation.
Syllabus
Week 1
Embeddings and Vector Databases
Text Embeddings and Semantic Search
In this Edureka course, learn how text embeddings power semantic search by representing meaning as vectors for more relevant and efficient information retrieval.
Vector Databases and Similarity Search
In this Edureka course, learn how vector databases and similarity search enable efficient retrieval of relevant information for Generative AI applications.
Week 2
Building and Advancing RAG Pipelines
The RAG Architecture — Retriever, Augmenter, Generator
In this Edureka course, explore the RAG architecture and learn how the retriever, augmenter, and generator work together to deliver relevant, context-aware responses.
Advanced Retrieval Techniques
In this Edureka course, learn advanced retrieval techniques to improve the relevance, accuracy, and efficiency of information retrieval in RAG applications.
Week 3
Evaluating and Shipping RAG Systems
Measuring RAG Quality
In this Edureka course, learn how to measure RAG quality using key evaluation approaches to assess retrieval accuracy, response relevance, and overall system performance.
Improving Pipelines and the Capstone
In this Edureka course, learn how to improve RAG pipelines and apply your knowledge to build a practical capstone project.
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Learning on this course
On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.
What will you achieve?
By the end of the course, you‘ll be able to...
- Explain how embeddings, vector databases, semantic similarity, and Retrieval-Augmented Generation support grounded LLM responses.
- Apply document loading, chunking, embedding, indexing, and retrieval techniques to build a functional RAG pipeline.
- Assess retrieval performance using re-ranking, query transformation, hybrid search, and specialised retriever strategies.
- Evaluate RAG quality using retrieval metrics, faithfulness, relevance, answer correctness, and RAGAS-based assessment.
- Create an end-to-end document-based knowledge assistant that delivers accurate, grounded, and context-aware responses.
Who is the course for?
This course is designed for learners who can build LLM applications with APIs and frameworks and who now want to build accurate, knowledge-grounded systems.
It is ideal for developers building retrieval-based assistants, data professionals creating document Q&A tools, and engineers preparing for RAG-focused LLM roles. Learners should be comfortable with LLM APIs and LangChain or LlamaIndex before starting. By the end, they will be able to design, build, and evaluate production-quality RAG systems.
Who will you learn with?
Who developed the course?
Edureka
Edureka is a global online learning platform offering industry-focused training in emerging technologies such as Cloud, AI, GenAI, Data Science, DevOps, and Cybersecurity, helping professionals upskill and advance their careers.
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