Edureka

Video RAG Foundations

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Edureka

Video RAG Foundations

This course is part of Video RAG Specialization

Edureka

Instructor: Edureka

Included with Coursera PlusLearn more

Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

5 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

5 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Explain retrieval augmented generation and build a document-based RAG chatbot.

  • Extract frames, audio, transcripts, captions, and OCR text from raw video.

  • Combine multimodal signals into structured, timestamped video records.

  • Generate multimodal embeddings and build a natural-language video search application.

Details to know

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Recently updated!

September 2026

Assessments

6 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the Video RAG Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 3 modules in this course

This module introduces the foundations of Retrieval-Augmented Generation and explores how VideoRAG extends traditional RAG workflows to process and retrieve information from video content. Learners explore VideoRAG architecture, key components, and supporting frameworks used to connect visual, audio, and textual information for more contextual and grounded AI responses.

What's included

7 videos3 readings2 assignments

This module focuses on preparing raw video content for retrieval by transforming unstructured video data into structured and searchable information. Learners explore video processing workflows, including frame extraction, transcription, scene segmentation, chunking strategies, and metadata generation to create effective video knowledge sources for VideoRAG systems.

What's included

5 videos2 readings2 assignments

This module introduces embeddings, vector databases, and semantic search techniques that enable efficient retrieval in VideoRAG systems. Learners explore how video content is converted into meaningful representations, stored, and searched using similarity-based retrieval approaches to identify relevant information and support accurate AI-generated responses.

What's included

7 videos2 readings2 assignments

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Instructor

Edureka
Edureka
244 Courses220,055 learners

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