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Introduction to Retrieval Augmented Generation (RAG)

Alfredo Deza

In this 2-hour project-based course, you will learn how to import data into Pandas, create embeddings with SentenceTransformers, and build a retrieval augmented generation (RAG) system with your data, Qdrant, and an LLM like Llamafile or OpenAI. This hands-on course will teach you to build an end-to-end RAG system with your own data using open source tools for a powerful generative AI application.

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Syllabus

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Build an end-to-end Retrieval Augmented Generation (RAG) system with your own data

Students learn a hands-on approach to using open source tools for a powerful generative AI application

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Reviews summary

Practical hands-on rag system introduction

According to learners, this course offers a highly practical and hands-on introduction to Retrieval Augmented Generation (RAG). Many appreciate its project-based approach, which facilitates quick understanding and implementation of an end-to-end RAG system using open-source tools like Qdrant and Llamafile. It's often highlighted as a gem for professionals seeking a quick skill upgrade in a rapidly evolving field. While generally well-received for its clarity, some note its fast-paced nature and express a desire for more in-depth coverage of advanced topics or theoretical nuances. A few recent reviews also highlighted potential setup challenges due to the dynamic nature of the tools involved, suggesting a need for updates.

Delivers a focused, efficient overview suitable for quick skill acquisition.

"Perfect for a quick skill upgrade."

"In just two hours, I got a solid end-to-end RAG implementation."

"A good overview of RAG, perfect for a quick start to understanding."

The course excels in providing direct application through its project-based approach.

"Excellent hands-on introduction to RAG. The project-based approach was fantastic for quickly understanding the core concepts..."

"This course is a gem for professionals. In just two hours, I got a solid end-to-end RAG implementation."

"I appreciated the focus on open-source tools. Building a RAG system with Llamafile was insightful."

The course is fast-paced and assumes some background in relevant technical areas.

"It assumes some Python and machine learning background, which was fine for me."

"The course is fast-paced, but manageable if you already have some coding experience."

"Assumes too much prior knowledge for an 'introduction'."

Some learners faced difficulties with environment setup due to tool updates.

"I encountered some setup issues with Qdrant on my machine. Debugging took a significant chunk of the 2 hours."

"Completely outdated setup instructions by the time I took it. Llamafile had changed significantly."

"Spent more time fixing environment issues than learning. Needs urgent updates."

Some learners desired more advanced theory or topics beyond the introduction.

"It covers the basics, but it's really an 'introduction.' I was hoping for more in-depth discussion on advanced RAG techniques or evaluation metrics."

"Too basic. Doesn't go deep enough into theory or practical challenges. Felt more like a demo than a comprehensive course."

"Could use a bit more depth on optimization, but for an intro, it's solid."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Introduction to Retrieval Augmented Generation (RAG) with these activities:

Review the fundamentals of natural language processing (NLP)

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Understand the concepts of NLP to prepare for the course's in-depth exploration of RAG systems.

Browse courses on NLP

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Review online materials on NLP concepts
Go through tutorials on basic NLP tasks like text classification and sentiment analysis

Explore SentenceTransformers library

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Gain familiarity with the SentenceTransformers library, which will be used to create embeddings in the course.

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Follow tutorials on installing and using SentenceTransformers
Experiment with different pre-trained models for sentence embeddings
Create custom sentence embeddings for a small dataset

Practice using Qdrant for vector similarity search

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Gain proficiency in using Qdrant, a vector database essential for building RAG systems.

Browse courses on Qdrant

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Create a Qdrant instance
Insert and search vectors in Qdrant
Optimize queries for better performance

Four other activities

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Build a simple text embedding model

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Apply the concepts learned in the course by building a basic text embedding model from scratch.

Browse courses on Embeddings

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Choose a pre-trained word embedding model
Develop a simple neural network architecture for text embedding
Train the model on a small dataset
Evaluate the model's performance

Attend a peer discussion session on RAG applications

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Engage with peers to discuss and learn about practical applications of RAG systems.

Browse courses on RAG

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Join a peer discussion group
Share knowledge and experiences
Provide and receive feedback

Contribute to an open-source RAG project

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Immerse in the practical aspects of RAG development by collaborating on an open-source project.

Browse courses on RAG

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Identify an open-source RAG project
Familiarize with the project's codebase
Contribute bug fixes or improvements

Develop a complete RAG system for a specific domain

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Integrate all the concepts covered in the course by building a comprehensive RAG system for a real-world application.

Browse courses on RAG

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Define the problem statement and domain
Collect and prepare data
Create embeddings using SentenceTransformers
Build a retrieval model using Qdrant
Develop a generative model using an LLM like Llamafile or OpenAI
Evaluate the system's performance

Career center

Learners who complete Introduction to Retrieval Augmented Generation (RAG) will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists combine programming ability with a deep understanding of data analysis to extract meaningful insights from data. Graduates with experience with embeddings, sentence transformers, and generative AI models may be especially well-suited to this role. This course gives an overview of how to use these tools and a hands-on implementation to build a retrieval augmented generation (RAG) system.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers develop and deploy machine learning models. Graduates with experience in building RAG systems will find the material on building embeddings, especially relevant. This course will also help build a foundation for working with large datasets and training models.

See salaries and explore the career path for Machine Learning Engineer

Natural Language Processing Engineer

Natural Language Processing Engineers develop and deploy models that understand and generate human language. Graduates with experience with sentence transformers and generative AI models may be especially well-suited to this role. This course will help build a foundation in these areas and provide hands-on experience building a RAG system.

See salaries and explore the career path for Natural Language Processing Engineer

Data Analyst

Data Analysts collect, clean, and analyze data to identify trends and patterns. This course will provide a solid foundation in data analysis techniques, as well as experience with embeddings, sentence transformers, and generative AI models. This combination of skills is increasingly valuable in the tech industry, especially for those interested in building data-driven products.

See salaries and explore the career path for Data Analyst

Software Engineer

Software Engineers design, develop, and maintain software systems. This course will provide a foundation in software engineering principles and practices, as well as experience with embeddings, sentence transformers, and generative AI models. This combination of skills is in high demand across a variety of industries.

See salaries and explore the career path for Software Engineer

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. This course will provide a foundation in quantitative analysis techniques, as well as experience with embeddings, sentence transformers, and generative AI models. This combination of skills is increasingly valuable in the financial industry, especially for those interested in building data-driven trading strategies.

See salaries and explore the career path for Quantitative Analyst

Product Manager

Product Managers define and manage the development of software products. This course will provide a foundation in product management principles and practices, as well as experience with embeddings, sentence transformers, and generative AI models. This combination of skills is in high demand across a variety of industries.

See salaries and explore the career path for Product Manager

Business Analyst

Business Analysts analyze business processes and systems to identify opportunities for improvement. This course will provide a foundation in business analysis techniques, as well as experience with embeddings, sentence transformers, and generative AI models. This combination of skills is increasingly valuable in the tech industry, especially for those interested in building data-driven products.

See salaries and explore the career path for Business Analyst

Project Manager

Project Managers plan and execute projects to achieve specific goals. This course will provide a foundation in project management principles and practices, as well as experience with embeddings, sentence transformers, and generative AI models. This combination of skills is in high demand across a variety of industries.

See salaries and explore the career path for Project Manager

Technical Writer

Technical Writers create documentation and other materials to explain technical concepts. This course will provide a foundation in technical writing principles and practices, as well as experience with embeddings, sentence transformers, and generative AI models. This combination of skills is increasingly valuable in the tech industry, especially for those interested in building data-driven products.

See salaries and explore the career path for Technical Writer

Information Architect

Information Architects design and organize websites and other digital products. This course will provide a foundation in information architecture principles and practices, as well as experience with embeddings, sentence transformers, and generative AI models. This combination of skills is increasingly valuable in the tech industry, especially for those interested in building data-driven products.

See salaries and explore the career path for Information Architect

Data Engineer

Data Engineers design and build data pipelines to collect, clean, and store data. This course will provide a foundation in data engineering principles and practices, as well as experience with embeddings, sentence transformers, and generative AI models. This combination of skills is increasingly valuable in the tech industry, especially for those interested in building data-driven products.

See salaries and explore the career path for Data Engineer

Database Administrator

Database Administrators manage and maintain databases. This course will provide a foundation in database administration principles and practices, as well as experience with embeddings, sentence transformers, and generative AI models. This combination of skills is increasingly valuable in the tech industry, especially for those interested in building data-driven products.

See salaries and explore the career path for Database Administrator

Systems Engineer

Systems Engineers design and build computer systems. This course may be useful for Systems Engineers interested in building data-driven systems. The course will provide a foundation in systems engineering principles and practices, as well as experience with embeddings, sentence transformers, and generative AI models.

See salaries and explore the career path for Systems Engineer

Network Engineer

Network Engineers design and build computer networks. This course may be useful for Network Engineers interested in building data-driven networks. The course will provide a foundation in network engineering principles and practices, as well as experience with embeddings, sentence transformers, and generative AI models.

See salaries and explore the career path for Network Engineer

Reading list

We've selected six books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Introduction to Retrieval Augmented Generation (RAG).

Cross-Lingual Word Embeddings

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This textbook provides a comprehensive introduction to deep learning techniques for NLP. It covers neural network architectures, training methods, and evaluation metrics specific to NLP tasks. It valuable resource for students and researchers interested in building deep learning models for NLP.

Cross-Lingual Word Embeddings (Synthesis Lectures...

Paperback

Revolutionizing Your Business with AI

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This website provides comprehensive information about OpenAI, its mission, and its research and development efforts. It also includes documentation for OpenAI's various products and services, including its language models.

Revolutionizing Your Business with AI: The Ultimate...

Hardcover

Revolutionizing Your Business with AI: The Ultimate...

Kindle Edition

Python for Data Analysis

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Practical guide to using Python for data analysis. It covers essential topics such as data manipulation, visualization, and machine learning. It useful resource for students and practitioners who need to work with data in Python.

Python for Data Analysis

Paperback

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Python for Data Analysis

Kindle Edition

Check price