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Learn Retrieval Augmented Generation

Build a RAG pipeline instead of just prompting harder: chunk data, search it, retrieve it, and feed it to an LLM.

Browse Lessons (read-only)

What will you learn?

Learn all about modern search engine techniques like keyword, vector, semantic and LLM-enhanced search. In this course you'll implement different search techniques from scratch in Python everything from simple keyword search up to a fully functional Retrieval Augmented Generation (RAG) pipeline using the Gemini API.

Chapter List

1
Preprocessing
Normalize and clean raw corpora so they are ready for indexing and downstream retrieval tasks.
2
TF-IDF
Construct inverted indexes and weighting schemes so keyword search can rank documents effectively.
3
Keyword Search
Tune keyword retrieval with BM25 refinements and metadata boosts to improve lexical relevance.
4
Semantic Search
Apply embeddings, similarity metrics, and vector databases to deliver semantic retrieval and RAG responses.
5
Chunking
Partition documents into context-preserving segments so RAG pipelines can retrieve the right snippets efficiently.
6
Hybrid Search
Blend lexical and semantic scores into unified retrieval pipelines that boost ranking quality.
7
LLMs
Leverage large language models to expand queries, correct intent, and orchestrate retrieval workflows.
8
Reranking
Re-score retrieved candidates with rerankers to surface the most relevant answers.
9
Evaluation
Measure retrieval precision, recall, and relevance so you can systematically improve RAG performance.
10
Augmented Generation
Combine retrieved context with LLMs to synthesize coherent, grounded answers for end users.
11
Agentic
Deploy autonomous agents that iteratively refine queries and navigate complex retrieval workflows.
12
Multimodal
Extend RAG to images and other modalities with multimodal embeddings and cross-modal retrieval.

Join 3,256 students in the Learn Retrieval Augmented Generation course

Read reviews of their learning experiences

Helpful ideas in building a good search engine for a specialized purpose, but the solutions are rough

(2/5)
Cyberis profile image

Cyberis

United States

I learn't a lot about different search techniques I didnt know existed before now

(5/5)
Johnpaul Chiwetelu profile image

Johnpaul Chiwetelu

Lagos, Nigeria

This course is excellent for learning the fundamentals of search, starting from traditional keyword-based search and progressing to semantic search using embeddings. It also covers combining different approaches through hybrid search, as well as image-based search. The course demonstrates how LLMs can be used to enhance search systems. Overall, it provides a comprehensive overview of the evolution of search technologies, and I learned a great deal from it.

(5/5)
Mani Yadla profile image

Mani Yadla

Hyderabad

challenging without feeling undoable

(5/5)
Karim Khavazov profile image

Karim Khavazov

Germany

A fantastic guided work-through of building a simple RAG system.

(5/5)
JAMES T profile image

JAMES T

Australia

This is the best RAG course I have taken.

(5/5)
Femi Ayodeji profile image

Femi Ayodeji

Nigeria

Great course! I learned a ton.

(5/5)
Mark R. Baker profile image

Mark R. Baker

North Saanich, BC, Canada

Fantastic course with real-world use! Strikes the right balance between guidance and handholding.

(5/5)
OK-Kokei profile image

OK-Kokei

Ireland

In this course, I built a RAG system from scratch. This was my first experience with the LLM APIs, and it felt smoothly!

(5/5)
Yury Zabegaev profile image

Yury Zabegaev

Norway

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Frequently asked Questions

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Yes! It's free to create an account and start learning. You'll get all the immersive and interactive features for free for a few chapters. After that, if you still haven't paid for a membership, you'll be in read-only (content only) mode.

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