Description

Building RAG Applications with LangChain course. This course teaches developers how to design and implement Retrieval-Augmented Generation (RAG) pipelines from scratch to production-ready using the LangChain framework. Retrieval -Augmented Generation (RAG) is a key paradigm for building AI applications that produce accurate and well-documented answers. Large Language Models are powerful, but they also have limitations, and their performance depends on the information they have access to. In this course, Building RAG Applications with LangChain, participants will gain the skills needed to design, build, and optimize production-ready RAG pipelines. Learners will first learn the core concepts of RAG architecture, document processing, embeddings, and information retrieval. Next, advanced retrieval techniques including Query Transformation, Multi-Step Retrieval, Self-Querying Retrievers, and Metadata Filtering will be explored to improve retrieval quality. Finally, how to fully optimize RAG pipelines through effective context management, prompt engineering, and building reliable workflows for response generation will be taught. By the end of this course, participants will have the skills and knowledge to build scalable, accurate, and functional RAG applications using LangChain.

What you will learn

• Developing end-to-end, production-ready RAG pipelines using LangChain
• Understand the main concepts of RAG architecture, document processing, embeddings, and information retrieval.
• Implement advanced retrieval techniques such as query transformation and multi-stage retrieval.
• Using self-query retrievers and metadata filtering to improve retrieval quality
• Effectively manage context and perform prompt engineering to optimize model performance
• Building reliable workflows to produce accurate and documented responses in AI applications

This course is suitable for people who:

• Software developers who want to build advanced AI applications based on large language models
• Data scientists and AI engineers looking to master RAG patterns and the LangChain framework
• Programmers who want to improve their knowledge in natural language processing and data retrieval
• People who want to extend the capabilities of large language models by connecting to proprietary knowledge bases

Course details

• Publisher: Pluralsight
• Instructor: Sandy Ludosky
• Education level: Intermediate
• Training duration: 2 hours and 25 minutes

Course headings

Images from the Building RAG Applications with LangChain course

Sample course video

Download link

Download file – 396 MB

Rapidgator link

Download File – 396 MB

File(s) password: www.downloadly.ir

File size

396 MB