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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.

What you'll learn in this Python course

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 free models on OpenRouter (via the OpenAI SDK).

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 4,132 students in the Learn Retrieval Augmented Generation course

Read reviews of their learning experiences

It was a wonderful course, so many abstractions of langchain have become clearer to me and my python skills have also improved. Easily a 10/10 course and also the most hardest bootdev course I have done till date, loved it.

(5/5)
Anshul Nema profile image

Anshul Nema

India

This is one of the best courses I've ever taken. I learned more than just RAG in this course. This project taught me about organizing a project, making a command line project in Python, and much more. This is one of the best courses on this platform that will teach you very relevant skills in this age.

(5/5)
Logan Moser profile image

Logan Moser

United States

Truly great course, taught me what my university course didn't (hands-on implementation). Tip: use a local model if you can, you'll also learn more this way ;)

(5/5)
Aduneer  profile image

Aduneer

Germany

the material was dense but worth it

(4/5)
John Noriega profile image

John Noriega

Denver, Co

Difficult and frustrating at times, but interesting and informative as well. Glad I pushed through to the end

(5/5)
T  profile image

T

United States

This course is very good for beginners

(5/5)
Anand Jaiswal profile image

Anand Jaiswal

Kushinagar, Uttar Pradesh, India

So far, this has been the most challenging course for me on Boot.dev. It's been a lot of fun learning about how intricate search is and how much LLMs have helped improve the practice.

(5/5)
Peter Schwenk profile image

Peter Schwenk

Newark, DE, USA

This was a very difficult but very rewarding class.

(5/5)
Mike Sopko profile image

Mike Sopko

United States

Worth the time and effort! Detailed and fulled of knowledge.

(5/5)
Jose Barrera profile image

Jose Barrera

TX

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Mediocrity doesn't cut it anymore

The only way to become a great developer is to write a lot of code

Avoid tutorial hell

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Stay motivated with

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Build portfolio projects

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Delve deeper

into foundational concepts

Learn flexibly online

without interrupting your life

For 1% the price of college

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Frequently asked Questions

Got questions? We've got answers

RAG means finding the documents relevant to a question and putting them in an LLM's prompt, so it answers from your data instead of its training. The retrieval step is the hardest part, which is why most of this course is about search.