Learn Retrieval Augmented Generation

Name: Learn Retrieval Augmented Generation [Full Course]
Availability: InStock
Author: Isaac Flath

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

Author

Isaac Flath

Isaac Flath was the Head of Data Science consulting at Centriam beforing making the switch to speaking and education in 2025. He's a data and AI specialist that has been using Python to build custom AI powered solutions for companies that want to understand their own information better.

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Join 964 students from companies like

40 Hours

of content

75 Addicting

lessons

Earn a certificate

of completion

Learn online

at your pace

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

Preprocessing

Normalize and clean raw corpora so they are ready for indexing and downstream retrieval tasks.

TF-IDF

Construct inverted indexes and weighting schemes so keyword search can rank documents effectively.

Keyword Search

Tune keyword retrieval with BM25 refinements and metadata boosts to improve lexical relevance.

Semantic Search

Apply embeddings, similarity metrics, and vector databases to deliver semantic retrieval and RAG responses.

Chunking

Partition documents into context-preserving segments so RAG pipelines can retrieve the right snippets efficiently.

Hybrid Search

Blend lexical and semantic scores into unified retrieval pipelines that boost ranking quality.

LLMs

Leverage large language models to expand queries, correct intent, and orchestrate retrieval workflows.

Reranking

Re-score retrieved candidates with rerankers to surface the most relevant answers.

Evaluation

Measure retrieval precision, recall, and relevance so you can systematically improve RAG performance.

Augmented Generation

Combine retrieved context with LLMs to synthesize coherent, grounded answers for end users.

Agentic

Deploy autonomous agents that iteratively refine queries and navigate complex retrieval workflows.

Multimodal

Extend RAG to images and other modalities with multimodal embeddings and cross-modal retrieval.

Join 964 students in the Learn Retrieval Augmented Generation course

Read reviews of their learning experiences

Definitely take this course! Actually covers a lot more than just RAG.

(5/5)

Adam Matthes

United States

This is a really in-depth course. It doesn't shy away from teaching you the fundamentals and nor doe...

(5/5)

Nachiket Kandari

India

Hey, not bad, looks like a legit project for a portfolio to me.

(5/5)

Regis Woof

France

many advanced topics covered, I think an interviewer would be impressed if a candidate would know al...

(5/5)

Aleksa Elezovic

Serbia

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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.