Learn the essentials of Retrieval-Augmented Generation (RAG) to enhance text generation accuracy. This path covers RAG basics, vector databases, semantic retrieval with embeddings, and building a complete RAG pipeline.

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Course 1

Introduction to RAG

3 lessons

8 practices

Learn what Retrieval-Augmented Generation (RAG) is, why combining retrieval with generation can reduce hallucinations, and how a basic RAG workflow contrasts with naive prompting. This course is mostly informational, setting the stage for more hands-on work in later courses.

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Course 2

Text Representation Techniques for RAG Systems

4 lessons

Course 3

Scaling up RAG with Vector Databases

4 lessons

Course 4

Beyond Basic RAG: Improving our Pipeline

4 lessons

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Tech consultant

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Jonathan Miller

Senior Machine Learning Engineer

+12

I'm impressed by the quality and can't stop recommending it. It's also a lot of fun!

Francisco Aguilar Meléndez

Data Scientist

+11

From our community

Hear what our customers have to say about CodeSignal Learn

I'm impressed by the quality and can't stop recommending it. It's also a lot of fun!

Francisco Aguilar Meléndez

Data Scientist

+11

I love that it's personalized. When I'm stuck, I don't have to hope my Google searches come out successful. The AI mentor Cosmo knows exactly what I need.

Faith Yim

Software Engineer

+14

It's an amazing product and exceeded my expectations, helping me prepare for my job interviews. Hands-on learning requires you to actually know what you are doing.

Alex Bush

Full Stack Engineer

I'm really impressed by the AI tutor Cosmo's feedback about my code. It's honestly kind of insane to me that it's so targeted and specific.

Abbey Helterbran

Tech consultant

I tried Leetcode but it was too disorganized. CodeSignal covers all the topics I'm interested in and is way more structured.

Jonathan Miller

Senior Machine Learning Engineer

+12

I'm impressed by the quality and can't stop recommending it. It's also a lot of fun!

Francisco Aguilar Meléndez

Data Scientist

+11

Learn essential text representation methods for RAG systems, from Bag-of-Words to embeddings. Explore how these techniques enhance understanding and retrieval, visualize embeddings with t-SNE, and compare BOW and embeddings in document retrieval and semantic search.

Discover how to scale RAG systems using vector databases. Learn to preprocess documents, store embeddings in ChromaDB, retrieve relevant chunks, and construct prompts. Manage updates and large-scale ingestion with batch strategies for efficient retrieval.

Enhance your RAG pipeline with advanced retrieval techniques. Implement hybrid retrieval, iterative query refinement, and context summarization. Constrain LLM outputs to retrieved context, ensuring accuracy and minimizing hallucinations in final responses.