End to end RAG Application Development with LangChain and Streamlit

  • IntermediateLevel

  • 1790+Students Enrolled

  • 30 MinsDuration

  • 4.5Average Rating

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About this Course

  • Build end-to-end RAG app development with LangChain & Streamlit, from data ingestion and retrieval to UI, so you can ship production-ready AI assistants and search tools.
  • Explore RAG fundamentals: chunking, embeddings, retrieval and evaluation, while building Streamlit RAG LangChain dashboards for accurate answers and strong user engagement.
  • Gain hands-on experience building and deploying RAG applications with LangChain, wiring Streamlit UIs, vector stores and APIs so you can ship reliable, AI-driven apps end to end.

Learning Outcomes

Learn RAG Concepts

Deep dive into Retrieval-Augmented Generation (RAG) & its uses

Work with LangChain

Build AI-powered apps with LangChain for seamless data retrieval.

Build Interactive AI Apps

Build user-friendly, real-time apps with Streamlit for engagement.

Who Should Enroll

  • Beginner to intermediate developers eager to learn RAG app development with LangChain and Streamlit for real projects.
  • Data scientists and ML engineers wanting practical Streamlit RAG LangChain skills to build search, chatbot, and QA apps.
  • Tech professionals and students exploring GenAI, RAG development, and end-to-end AI apps using LangChain with Streamlit.

Course Curriculum

Learn end to end RAG app development with LangChain and Streamlit, covering data ingestion, embeddings, vector search, retrieval, evaluation, and deployment as interactive AI-powered apps.

tools

  1. 1. End to End RAG Application Development

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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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
certificate

Frequently Asked Questions

Looking for answers to other questions?

A RAG application combines retrieval from your own data with a generative model. Instead of relying only on the model’s memory, it pulls relevant documents first, then generates answers. This makes responses more accurate, grounded, and context-aware for real-world use cases.

You can build knowledge-base assistants, document Q&A tools, internal search copilots, and domain-specific chatbots. By the end, you’ll be able to design and deploy RAG applications with LangChain and Streamlit tailored to your own datasets and business scenarios.

LangChain simplifies building retrieval pipelines, prompt chains, and RAG logic, while Streamlit makes it easy to create interactive, production-like UIs. Together, they let you prototype, test, and deploy RAG app development projects quickly without heavy frontend work.

RAG first retrieves relevant documents from your data, then passes them to the LLM for generation. This grounding step helps reduce hallucinations, keeps answers context-aware, and makes your RAG app development more reliable for real use cases.

Chunking breaks large documents into smaller, meaningful pieces. Good chunking improves retrieval quality, keeps context focused, and reduces token usage. In RAG development, effective chunk design directly impacts how accurately your LangChain and Streamlit RAG app answers questions.

Embeddings convert text into numerical vectors that capture semantic meaning. In a rag application with LangChain, these vectors power similarity search, allowing the system to retrieve the most relevant chunks before generating answers in your Streamlit interface.

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