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Retrieval-Augmented Generation (RAG): Theory and Python Implementation

Name: Retrieval-Augmented Generation (RAG): Theory and Python Implementation
Price: 299 INR
Availability: InStock

This course covers the foundations of retrieval-augmented generation (RAG), from its origins and core retrieval and generation phases to classification and evaluation. You'll gain hands-on experience building RAG pipelines using LlamaIndex and LangChain, and learn how to assess their effectiveness in real-world applications.

What You'll Learn

Explain the core workflow of RAG, including data loading, parsing, embedding, and indexing.

Describe the retrieval and generation phases and how they combine to produce responses.

Classify different RAG architectures based on key components.

Implement classic RAG pipelines using LlamaIndex.

Build RAG pipelines with LangChain.

Evaluate RAG systems for effectiveness in real-world applications.

About this course

Retrieval-Augmented Generation (RAG): Theory and Python Implementation is a self-paced beginner-level technology online course on GreyAcademy, taught by Anurag Trivedi. It has 2 modules and 3 chapters, and it takes about 7 hours to complete. Enrol once and learn at your own pace. You get a completion certificate when you finish all the lessons.

Topics covered: Filippo Bianchini, Generation Phase, Practical Application—Langchain.

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