Build a QA RAG system with Langchain

IntermediateLevel
516+Students Enrolled
30 MinsDuration
5Average Rating

About
Learning Outcomes
Who Should Enroll
Course Curriculum
Instructors

About this Course

Learn to build a QA RAG system with LangChain using Wikipedia data. Master loading, chunking, embedding, and storing content in a vector DB to deliver accurate retrieval-based ans.
Understand how to design an effective retrieval strategy beyond basic cosine similarity. Learn how LangChain connects retrievers to a RAG chain for generating precise answers.
Gain hands-on experience creating a full rag application indexing document chunks, integrating embeddings, & deploying a QA RAG workflow that delivers reliable, real-world problem.

Learning Outcomes

Learn RAG System Design

Learn to build a powerful QA RAG system using LangChain.

Deep Dive into LangChain

Gain in-depth knowledge of LangChain for data retrieval and NLP in AI.

Hands-On RAG Experience

Build and deploy QA RAG systems, enhancing real-world AI skills.

Who Should Enroll

Beginners and developers who want to learn how to build practical QA RAG systems using LangChain.
AI, ML, and data professionals seeking hands-on experience in retrieval, generation, and RAG workflows.
Students and tech enthusiasts aiming to understand real-world RAG applications and deploy QA solutions.

Course Curriculum

Learn to build a QA RAG system with LangChain, covering data ingestion, embeddings, vector stores, retrieval pipelines, prompt design, evaluation, and deployment.

1. Hands On: Build a QA RAG System with Langchain
2. Course Handouts

Meet the instructor

Our instructor and mentors carry years of experience in data industry

Dipanjan Sarkar

Principal AI Scientist

Dipanjan Sarkar, Head of AI, Author & Consultant, has 12+ years of expertise in ML, DL, GenAI, CV & NLP. He has led AI initiatives across Fortune 100 firms & startups, building data products & upskilling professionals at all levels.

Get this Course Now

With this course you’ll get

30 Mins
Duration
Dipanjan Sarkar
Instructor
Intermediate
Level

Certificate of completion

Earn a professional certificate upon course completion

Industry-Recognized Credential
Career Advancement Credential
Shareable Achievement

Frequently Asked Questions

Looking for answers to other questions?

A QA RAG system combines information retrieval with language generation to answer questions using external knowledge. It fetches the most relevant document chunks and uses an LLM to generate accurate, context-aware responses, improving precision over standalone language models.

Chunking breaks long Wikipedia articles into manageable pieces, making retrieval more accurate. Embeddings convert each chunk into numerical vectors that capture semantic meaning. This combination helps the system quickly identify the most relevant information for a given query.

A vector database stores all chunk embeddings and enables fast similarity search. Instead of scanning entire documents, the system compares vector distances to retrieve the most relevant chunks, significantly improving the speed and quality of question-answering.

Basic cosine similarity may not always surface the best information. Advanced strategies like maximal marginal relevance or hybrid search, balance relevance and diversity, ensuring that the model receives richer context and generates more accurate, grounded responses for user queries.

LangChain simplifies the entire workflow by providing tools to load data, chunk text, generate embeddings, manage vector stores, and connect retrievers to LLMs. It creates a structured, modular pipeline that speeds up development of reliable RAG systems.

Once relevant chunks are retrieved, the RAG chain feeds them into a language model that synthesizes the information into coherent, natural responses. This grounding ensures answers are fact-based, contextually aligned, and closer to how a human would explain the information.

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