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About this course
Learn RAG and Agents to upskill in Generative AI
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Module 1
4 Parts
- 0:45 Hr
Meaning of RAG, its application and drawbacks.
10 Min
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Understanding the RAG workflow
15 Min
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To retrieve or finetune?
10 Min
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Well, now that we know the difference let's see how Agents work!
10 Min
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Module 2
4 Parts
- 0:30 Hr
Now, let's explore Chunking strategies within the RAG pipeline and methods to optimize them.
10 Min
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Embeddings and vector databases are crucial components of an RAG pipeline and are closely related
5 Min
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Where do the vector embeddings live?
10 Min
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Llama Index is like a super smart librarian.
5 Min
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Module 3
3 Parts
- 0:35 Hr
Langchain can accommodate nearly any LLM with just an API key or token. Let's learn more about it.
10 Min
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Priorities!
10 Min
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A key component of RAG is the use of hybrid search, which blends traditional keyword-based search with semantic search techniques.
15 Min
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