RAG with LangChain Course in Delhi

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25 hrs of E-Learning Videos
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RAG with LangChain Course Overview

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Upcoming RAG with LangChain Batches

21-09-2026
Mon-FriWeekdays Regular
08:00 AM & 10:00 AM Batches(Class 1Hr - 2Hrs) / Per Session
23-09-2026
Mon - FriWeekdays Regular
06:00 PM & 08:00 PM Batches(Class 1Hr - 2Hrs) / Per Session
25-09-2026
Sat-SunWeekend Batch
09:00 AM & 01:00 PM Batches(Class 2Hr - 4Hrs) / Per Session
Can't find a batch? Pick your own schedule

RAG with LangChain Course Syllabus

Learn to build production-ready Retrieval-Augmented Generation (RAG) applications using LangChain and modern LLM technologies. Master document processing, embeddings, vector databases, retrieval strategies, prompt engineering, and conversational RAG workflows. Build real-world AI applications using LangChain, LLMs, vector stores, evaluation techniques, and deployment best practices.

  • Introduction to Generative AI and Large Language Models
  • LLM architecture and capabilities
  • What is RAG?
  • RAG architecture and workflow
  • RAG vs. fine-tuning
  • Real-world RAG use cases
  • Introduction to LangChain
  • LangChain architecture and ecosystem
  • Models, prompts and messages
  • Chains and runnable components
  • LangChain Expression Language (LCEL)
  • Building a basic LLM application
  • Loading PDFs, Word, CSV and web data
  • LangChain document loaders
  • Text cleaning and preprocessing
  • Document metadata
  • Handling large document collections
  • Data preparation for RAG
  • Why document chunking matters
  • Recursive and semantic text splitting
  • Chunk size and overlap optimization
  • Embedding fundamentals
  • Open-source and API-based embedding models
  • Measuring embedding similarity
  • Vector database fundamentals
  • FAISS, Chroma and other vector stores
  • Creating and storing embeddings
  • Similarity search
  • Metadata filtering
  • Building retrievers with LangChain
  • Creating a complete RAG pipeline
  • Retriever and document chain
  • Prompt templates
  • Context injection
  • Answer generation
  • Source-document retrieval
  • Building a basic RAG chatbot
  • Semantic vs. keyword search
  • Hybrid retrieval
  • Multi-query retrieval
  • Query rewriting
  • Contextual compression
  • Reranking
  • Parent-document retrieval
  • Conversational RAG architecture
  • Chat history and memory concepts
  • LangChain agents
  • Tool calling
  • Agentic retrieval workflows
  • Multi-step question answering
  • Building intelligent AI assistants
  • RAG evaluation fundamentals
  • Retrieval and response quality metrics
  • Hallucination reduction
  • RAG evaluation with modern frameworks
  • Prompt injection and data security
  • Latency and cost optimization
  • Production troubleshooting
  • Building RAG APIs
  • FastAPI integration
  • Docker and deployment concepts
  • Monitoring and observability
  • Scaling RAG applications
  • Enterprise RAG architecture
  • Multimodal and Agentic RAG trends
  • Capstone project implementation
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Hands On RAG with LangChain Projects

Enroll in our RAG with LangChain classes, where our course focuses on providing high-quality training with a strong foundation in core concepts and a practical approach. Through exposure to current industry use cases and scenarios, participants will enhance their skills and gain the ability to execute real-time projects using best practice

PDF Document Q&A Chatbot with LangChain

  • Upload and process multiple PDF documents
  • Implement document chunking and embeddings
  • Store embeddings in a vector database
  • Build a LangChain RAG pipeline
  • Generate context-grounded answers with source references

Enterprise Knowledge Base Assistant

  • Integrate multiple enterprise documents and data sources
  • Implement semantic and hybrid retrieval
  • Add metadata-based filtering
  • Implement conversational question answering
  • Evaluate and optimize retrieval accuracy

Agentic RAG Research Assistant

  • Build an intelligent LangChain-based agent
  • Combine RAG with external tools and knowledge sources
  • Implement query planning and multi-step retrieval
  • Add tool calling and structured responses
  • Deploy the application as a production-ready AI assistant

For Corporates

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Our Instructor
Name
Mr. Krishnan
Experience
7+ Years
Specialized in
Specialized in Generative AI, LangChain, RAG, Large Language Models, AI Agents, Vector Databases, Prompt Engineering, and production-grade AI application development.
More Details
The trainer specializes in developing practical Generative AI applications using LangChain, LLMs, RAG architectures, and AI agents. Training combines conceptual learning with hands-on coding, real-world architecture, debugging, evaluation, and deployment practices. Participants gain practical experience by developing multiple end-to-end projects aligned with modern 2026 AI application development.

RAG with LangChain Training Objectives

Our Best RAG with LangChain Training aims to empower participants with comprehensive skills and practical knowledge in this field. Objectives include mastering core concepts, applying skills through real-world projects, critical thinking, and tackling professional challenges. This enhances career development and contributes to industry advancement.

Basic to intermediate Python knowledge is recommended for this course. You should be comfortable with variables, functions, classes, APIs, packages, and basic programming concepts. Advanced Machine Learning knowledge is not mandatory because the RAG concepts are taught step by step.

The course covers LangChain, LLMs, embedding models, vector databases, retrievers, prompt templates, and AI agents. It also introduces technologies and techniques such as FAISS, Chroma, hybrid search, reranking, and RAG evaluation. The technology stack can be adapted to current models and LangChain ecosystem developments during 2026.

You will build three practical applications: a PDF Q&A Chatbot, an Enterprise Knowledge Base Assistant, and an Agentic RAG Research Assistant. The projects cover document processing, vector search, conversational RAG, advanced retrieval, and LangChain agents.

RAG is an AI application architecture that retrieves relevant information before generating an answer. LangChain is a framework/toolkit that provides components for building RAG pipelines and other LLM applications. In simple terms, RAG describes the approach, while LangChain can be used to implement that approach.

Yes, LangChain can be used to develop applications such as knowledge assistants, document chatbots, customer-support systems, and research tools. Production systems require additional attention to security, evaluation, monitoring, scalability, latency, and cost.

Yes, the course is structured to take learners from RAG fundamentals to advanced LangChain implementations. Participants progressively learn LLMs, embeddings, vector databases, retrieval, chains, agents, and evaluation.

Job Assistance Program

Our Job Assistance Programme offers you special guidance through the course curriculum and helps in your interview preparation.

Specialised Curriculum
Get on-field knowledge and skills from our expert instructors.
Assessment
Upgrade your on-field skills with our assessments and track your progress in real time.
Hands-on Project
Our hands-on project help you gain experience in real-time working.
Certification Guidance
A global certificate always helps you stand out from the crowd.
Portfolio Building
Experts guide you to maximise your profile with current industry trends that employers expect.
Placment Cell
We promote your abilities and showcase your portfolio to employers.

RAG with LangChain Career Opportunity

RAG with LangChain is the most common programming language, and it works on all computers and mobile devices without needing to be upgraded. It is one of the highest-paying careers in the software development industry, and those with the RAG with LangChain certification can earn an average of  8 LPA per year.

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Infibee’s placement guidance navigates you to your desired role in top organisations, ensuring you stand out and excel in every opportunity.

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RAG with LangChain Training FAQ's

Infibee RAG with LangChain Training offers wide range of services that suits for both fresher and experienced persons via both offline and online at your suitable time slots.

You need not worry about having missed a class. Our dedicated course coordinator will help them with anything and everything related to administration. The coordinator will arrange a session for the student with trainers in place of the missed one.

Yes, of course. You can contact our team at Infibee Technologies, and we will schedule a free demo or a conference call with our mentor for you.

We provide classroom, online, and self-based study material and recorded sessions for students based on their individual preferences.

Yes, all our trainers are industry professionals with extensive experience in their respective domains. They bring hands-on practical and real-world knowledge to the training sessions.

Yes, participants typically receive access to course materials, including recorded sessions, assignments, and additional resources, even after the training concludes.

We provide placement assistance to students, including resume building, interview preparation, and job placement support for a wide range of software courses.

Yes, we offer customisation of the syllabus for both individual candidates and corporate also.

Yes, we offer corporate training solutions. Companies can contact us for customised programmes tailored to their team’s needs.

Participants need a stable internet connection and a device (computer, laptop, or tablet) with the necessary software installed. Detailed technical requirements are provided upon enrollment.

In most cases, such requests can be accommodated. Participants can reach out to our support team to discuss their preferences and explore available options.

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