Ultimate RAG Bootcamp Using Langchain,LangGraph & Langsmith

Build powerful RAG pipelines: Traditional, Advanced, Multimodal & Agentic AI with LangChain,LangGraph and Langsmith


What you'll learn
- Build traditional RAG pipelines for accurate and efficient information retrieval.
- Implement advanced retrieval methods like hybrid search, multimodal RAG, and persistent memory.
- Design multi-agent and autonomous RAG systems using LangGraph for collaborative AI reasoning.
- Use LangSmith for tracking, debugging, and optimizing RAG workflows in real-world projects.
- Integrate LangSmith for tracking, debugging, and optimizing RAG performance.
- Use vector databases like FAISS, Pinecone, and Weaviate efficiently.
- Build domain-specific knowledge chatbots with hybrid search.
- Develop multimodal AI assistants that process both text and images.

Requirements
- Basic understanding of Python programming (variables, loops, functions).
- No prior knowledge of RAG is required — everything will be taught from scratch.
- Familiarity with AI concepts like LLMs is helpful
- Basic Knowledge Of Langchain

Description
Unlock the Power of Retrieval-Augmented Generation (RAG) – From Traditional to Advanced Agentic AI Systems

In today’s AI-driven world,Retrieval-Augmented Generation (RAG)is one of the most impactful and in-demand techniques, powering everything from intelligent chatbots and personal assistants to automated research agents and enterprise AI systems.

TheUltimate RAG Bootcampis your complete, step-by-step guide to mastering RAG using the latest and most powerful tools —LangChain,LangGraph, andLangSmith. Whether you’re an AI beginner or an experienced developer, this course takes you from the fundamentals of RAG pipelines all the way to advanced Agentic RAG architectures used in production by leading companies.

Why This Course?

Unlike other courses that only touch on basic RAG concepts, this bootcamp goes deeper. You will:

Learntraditional RAGstep-by-step.

Masteradvanced retrieval strategieslike hybrid search, vector optimization, and multimodal RAG.

Implementmulti-agent, autonomous AI pipelinesthat can think, plan, and act collaboratively.

UseLangSmithfor experiment tracking, debugging, and performance optimization.

Buildreal-world, deployable AI applicationsfrom start to finish.

By the end, you won’t just understand RAG — you’ll be able todesign, optimize, and deployadvanced AI systems for real-world scenarios.

What You’ll Learn

1. RAG Foundations

What RAG is and why it matters.

Traditional RAG architecture: data ingestion, parsing, embeddings, and retrieval.

Choosing and using vector databases effectively.

Building retrieval + generation workflows with LangChain.

2. Advanced RAG Techniques

Advanced chunking strategies for precision retrieval.

Hybrid search: combining vector and keyword search.

Multimodal RAG for text, images, and more.

Persistent memory for context retention.

Self-RAG for improving retrieval quality.

Adaptive & Corrective RAG for dynamic and error-resistant pipelines.

3. Agentic RAG Pipelines

Multi-agent architectures with LangGraph.

Designing agents for research, summarization, and decision-making.

Autonomous RAG with minimal human intervention.

Collaborative AI reasoning with specialized agents.

4. LangSmith for RAG Evaluation & Optimization

Tracking and managing RAG experiments.

Debugging retrieval pipelines and fixing bottlenecks.

Running evaluation metrics to boost accuracy.

5. Real-World RAG Projects

Chatbot with domain-specific knowledge.

Multi-agent research assistant for automated reports.

Multimodal AI assistant with text and image retrieval.

Deploying RAG applications to the cloud.

Who This Course Is For

AI developers & machine learning engineers.

Data scientists integrating retrieval systems.

Software developers building intelligent assistants.

Researchers exploring advanced RAG workflows.

Anyone aiming to master RAG from scratch to production-ready deployment.

Tools & Frameworks You’ll Master

LangChain– Build modular RAG pipelines.

LangGraph– Create advanced agent-based workflows with memory.

LangSmith– Track, debug, and evaluate RAG systems.

Vector Databases– FAISS, Pinecone, Weaviate, and more.

Cloud Deployment– Take AI apps from development to production.

Your Learning Journey

UnderstandRAG fundamentals.

Buildreal-world retrieval pipelines.

Advanceto agentic and autonomous AI systems.

Deployand monitor in production.

Optimizefor continuous improvement.

RAG is more than just an AI trend — it’s the foundation of intelligent, context-aware applications.

By the end of this bootcamp, you’ll have hands-on, production-ready skills to build and deploy cutting-edge RAG pipelines with LangChain, LangGraph, and LangSmith.

Join the Ultimate RAG Bootcamp today — and start building AI systems that truly understand, reason, and deliver results.

Who this course is for:
- AI developers & ML engineers who want to master RAG from basics to advanced agentic systems.
- Data scientists aiming to integrate retrieval systems into AI workflows.
- Software developers building intelligent assistants, chatbots, or research tools.
- Researchers exploring advanced RAG workflows and multi-agent AI pipelines.
- AI enthusiasts & beginners who want a hands-on, step-by-step approach to RAG without prior experience.
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