Data Sciencebeginner

Building a RAG Solution from Scratch

Name: Building a RAG Solution from Scratch
Availability: InStock

0 students 2h 13m 22 lectures

DCreated by Dafluma Instructor

Last updated Recently updated English Certificate of completion

What you will learn

The instructor has not published learning outcomes yet.

Requirements

No strict prerequisites listed.

Course Curriculum

7 sections. 22 lectures. 2h 13m total.

Section 1: Welcome

3 lectures

Getting the most out of this course3 min
Version check1 min

Section 2: Introduction to RAG Systems

3 lectures

What is Retrieval Augmented Generation RAG4 min
Key components of a RAG system8 min
Comparison with traditional LLM approaches5 min

Section 3: Creating the Vector Database

5 lectures

Demo Setting up the development environment6 min
Data preparation and preprocessing Techniques5 min
Creating the vector database5 min
Demo Implementing the vector database with MIMIC III11 min
Solution Building and using the vector database9 min

Section 4: Developing RAG for Chatbots

5 lectures

Designing a chatbot architecture with RAG8 min
Demo Integrating the generation component13 min
Demo Full integration and testing12 min
Optimization and evaluation techniques4 min
Solution Deploying a functional chatbot9 min

Section 5: Implementing RAG for Decision Support Systems

3 lectures

Designing decision support systems with RAG7 min
Demo Building the information retrieval system5 min
Demo Implementing decision support logic4 min

Section 6: Research Ethics and Future Directions

2 lectures

Current research and innovations in RAG7 min
Future trends in RAG development4 min

Section 7: Conclusion

1 lectures

Course summary and next steps5 min

Your Instructor

Dafluma Instructor

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Data Sciencebeginner

Building a RAG Solution from Scratch

0 students 2h 13m 22 lectures

DCreated by Dafluma Instructor

Last updated Recently updated English Certificate of completion

What you will learn

The instructor has not published learning outcomes yet.

Requirements

No strict prerequisites listed.

Course Curriculum

7 sections. 22 lectures. 2h 13m total.

Section 1: Welcome

3 lectures

Getting the most out of this course3 min
Version check1 min

Section 2: Introduction to RAG Systems

3 lectures

What is Retrieval Augmented Generation RAG4 min
Key components of a RAG system8 min
Comparison with traditional LLM approaches5 min

Section 3: Creating the Vector Database

5 lectures

Demo Setting up the development environment6 min
Data preparation and preprocessing Techniques5 min
Creating the vector database5 min
Demo Implementing the vector database with MIMIC III11 min
Solution Building and using the vector database9 min

Section 4: Developing RAG for Chatbots

5 lectures

Designing a chatbot architecture with RAG8 min
Demo Integrating the generation component13 min
Demo Full integration and testing12 min
Optimization and evaluation techniques4 min
Solution Deploying a functional chatbot9 min

Section 5: Implementing RAG for Decision Support Systems

3 lectures

Designing decision support systems with RAG7 min
Demo Building the information retrieval system5 min
Demo Implementing decision support logic4 min

Section 6: Research Ethics and Future Directions

2 lectures

Current research and innovations in RAG7 min
Future trends in RAG development4 min

Section 7: Conclusion

1 lectures

Course summary and next steps5 min

Your Instructor

Dafluma Instructor

Published course Dafluma instructor

Instructor profile details are managed by the backend. This course is taught by Dafluma Instructor and kept inside the platform subscription experience.

Course safety and support

Report misleading, unsafe, or rights-sensitive content. For access or payment issues, open a support request instead.

Get support

Report targetReasonDetails

You will be asked to sign in before submitting a report so the safety team can follow up responsibly.