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Learn Retrieval Augmented Generation

Master advanced search techniques and build a Retrieval Augmented Generation (RAG) pipeline in Python.

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 2,627 students in the Learn Retrieval Augmented Generation course

Read reviews of their learning experiences

not for the faint of heart. I did this at the end of the python track when all I had left was my capstone and it still pushed me to my limits.

(4/5)
Matthew Renner profile image

Matthew Renner

southern indiana

I now have a far greater understanding of how this works and had a kickass time while doing it. Bravo!

(5/5)
Divine-Beanbag profile image

Divine-Beanbag

United States

Very nice and detail course about RAG.

(5/5)
Si Thu San profile image

Si Thu San

Thailand

Very good introductory course to RAG!

(5/5)
Александр Щербаков profile image

Александр Щербаков

Serbia

The course is very good

(5/5)
Guilherme Sant'Ana profile image

Guilherme Sant'Ana

Brazil

Cannot recommend this one highly enough !

(5/5)
Ondrej Žigo profile image

Ondrej Žigo

Czechia

Pretty good course!

(5/5)
Rafael Alejandro Rojas Diaz profile image

Rafael Alejandro Rojas Diaz

Venezuela

WOW! great course. One simple change though would make this so much easier to complete. Use Ollama and Gemma3 versus Google AI online. I switched over due to token restrictions using the 3.3GB model on a modest computer (CPU only).

(5/5)
Damian Oslebo profile image

Damian Oslebo

United States

Great.

(5/5)
Angel Velasco profile image

Angel Velasco

México City

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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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