Building with LangChain & RAG

Name: Building with LangChain & RAG
Availability: InStock

Build production-ready LLM applications using LangChain. From core chains and memory management through document loading, vector stores, retrieval-augmented generation, and autonomous agents. Based on DeepLearning.AI's LangChain series: LangChain for LLM Application Development, Chat with Your Data, and Building Systems with the ChatGPT API.

4.70/5.0

9 hours

980 enrolled

Updated Apr 2026

Course Content ↓

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By CloudaQube Team

What You'll Learn

Build LLM applications using LangChain chains, prompts, and output parsers

Implement conversation memory for multi-turn AI applications

Load, split, and embed documents into vector stores for RAG

Build a production-ready document Q&A chatbot with source attribution

Evaluate RAG pipeline quality using RAGAS metrics

Create LangChain agents with custom tools using the ReAct pattern

Prerequisites

Python programming (intermediate level)
Basic understanding of LLMs and prompt engineering

About the Instructor

CloudaQube Team

Expert instructor with hands-on industry experience in Ai Ml.

Included in paid plans

LevelIntermediate

Duration9 hours

Lessons—

Students980

Rating4.70 / 5.0

This course includes

Hands-on practice labs
AI-powered explanations
Progress tracking
Certificate of completion
Lifetime access

14-day money-back guarantee