Course Objectives
1. What is the objective of the LangChain – Building LLM Applications course?
This course is designed to help learners build production-ready AI applications using LangChain, one of the leading open-source frameworks for developing Large Language Model (LLM) applications. You will learn prompt engineering, Retrieval-Augmented Generation (RAG), AI agents, vector databases, memory, tools, and LLM orchestration using modern AI technologies. LangChain provides integrations with major models and tools, enabling developers to create intelligent AI applications efficiently.
2. Who should enroll in this LangChain course?
This course is ideal for students, freshers, Python developers, AI engineers, Machine Learning engineers, Data Scientists, Software Developers, Full Stack Developers, Cloud Engineers, and professionals interested in Generative AI and LLM application development.
3. What practical skills will I gain from this training?
You will learn Python for AI, LangChain architecture, prompt engineering, RAG implementation, vector databases, embeddings, AI agents, memory management, document processing, API integration, LLM deployment, and chatbot development through real-world projects.
4. Does this course include real-time projects?
Yes. The training includes hands-on projects such as AI chatbots, document Q&A systems, enterprise knowledge assistants, AI search engines, customer support bots, and intelligent workflow automation using LangChain.
5. How will this course help my career?
After completing this course, you can pursue careers as a Generative AI Engineer, AI Application Developer, LangChain Developer, LLM Engineer, Prompt Engineer, Python AI Developer, Machine Learning Engineer, AI Solutions Architect, or NLP Engineer.
Popular Techniques Covered in This Course
LangChain Framework Development
Python Programming for AI
Prompt Engineering
Retrieval-Augmented Generation (RAG)
AI Agents & Tool Integration
Vector Databases & Embeddings
OpenAI & Google Gemini Integration
LLM Application Development
LangSmith & AI Workflow Management
AI Chatbot Development & Deployment
Get Hands-on Knowledge about Real-Time Projects
Project 1
1. Enterprise AI Chatbot
Build an intelligent chatbot capable of answering customer queries using OpenAI or Gemini with conversation memory and context-aware responses.
Project 2
2. Document Question-Answering System
Develop a RAG-based application that allows users to upload PDF, Word, and text documents and receive accurate AI-generated answers.
Project 3
3. AI Customer Support Assistant
Create an AI-powered customer support solution that integrates with company knowledge bases, FAQs, and CRM systems.
Project 4
4. Intelligent Resume Screening Application
Develop an AI application that analyzes resumes, matches candidates with job descriptions, ranks applicants, and generates interview insights.
Project 5
5. Multi-Agent Business Automation System
Build a LangChain-based AI agent system that performs research, summarizes documents, interacts with APIs, retrieves business data, and automates enterprise workflows.
Key Features
Edubrights offers LANGCHAIN – BUILDING LLM APPLICATIONS Training in virtual mode with expert trainers. Here are the key features,
40 Hours Course Duration
100% Job Oriented Training
Industry Expert Faculties
Free Demo Class Available
Completed 500+ Batches
Certification Guidance
Curriculum
Module 1: Introduction to LangChain
- What is LangChain and why it is the leading LLM framework
- LangChain architecture: chains, agents, memory, and tools
- Setting up the LangChain development environment
- Connecting LangChain to OpenAI, Anthropic, and open-source LLMs
Module 2: Chains and Pipelines
- LangChain Expression Language (LCEL) fundamentals
- Building sequential and parallel chains
- Prompt templates and output parsers
- Combining multiple LLM calls into structured workflows
Module 3: Retrieval-Augmented Generation (RAG)
- RAG architecture and when to use it
- Document loaders and text splitters
- Embedding models and vector store integration
- Building a RAG pipeline with FAISS and Chroma
Module 4: Vector Databases and Semantic Search
- Introduction to embeddings and similarity search
- Integrating Pinecone, Weaviate, and Chroma with LangChain
- Hybrid search: combining keyword and semantic retrieval
- Optimising retrieval for accuracy and performance
Module 5: LangChain Agents and Tools
- What are agents and how they reason with tools
- Built-in LangChain tools: search, calculator, Python REPL
- Building custom tools for domain-specific tasks
- ReAct and OpenAI Function-Calling agents
Module 6: Memory and Conversation Management
- Types of memory in LangChain: buffer, summary, and entity
- Building conversational chatbots with persistent memory
- Managing long conversation context efficiently
- Integrating memory with RAG pipelines
Module 7: LangSmith, Evaluation, and Deployment
- Tracing and debugging LangChain apps with LangSmith
- Evaluating LLM application quality and accuracy
- Deploying LangChain applications with FastAPI and LangServe
- Production best practices: caching, error handling, and monitoring
Module 8: Hands-on Projects and Assessment
- Building a document Q&A system with RAG
- Creating a multi-tool AI agent for a business use case
- End-to-end LLM application deployment project
- Final assessment and course certification
Receive Training From Our Skilled and Effective Trainers
Experience in the Industry Gain expertise from LangChain developers who have built production-grade RAG pipelines, AI agents, and multi-step LLM workflows for enterprise clients.
Backgrounds at the Top Our LangChain trainers have delivered AI application projects for leading technology companies and AI-first product startups globally.
Clear & Effective Teaching LangChain chains, agents, memory, and retrieval components are explained clearly with working code examples for every concept.
Hands-On Learning Focus Students build complete LLM-powered applications including document Q&A systems, AI agents, and RAG pipelines using LangChain and vector databases.
Up-to-Date Knowledge Trainers keep pace with LangChain's rapid release cycle, covering LangGraph, LCEL, and the latest framework updates in every cohort.
Become a Certified LangChain – Building LLM Applications Professional
Validate your expertise in LangChain, Generative AI, Large Language Models (LLMs), Prompt Engineering, Retrieval-Augmented Generation (RAG), AI Agents, Vector Databases, Embeddings, and enterprise AI application development.
Course FAQs
1. What is LangChain?
LangChain is an open-source framework for building applications powered by Large Language Models (LLMs). It helps developers integrate AI models, tools, databases, APIs, and workflows into intelligent applications.
2. Why should I learn LangChain?
LangChain simplifies the development of AI-powered chatbots, AI agents, RAG applications, document assistants, and enterprise AI solutions while supporting multiple LLM providers.
3. Is this course suitable for beginners?
Yes. The course starts with Python and Generative AI fundamentals before progressing to advanced LangChain concepts.
4. Do I need Python knowledge?
Basic Python knowledge is recommended. However, essential Python concepts required for LangChain development are covered during the course.
5. What topics are covered in this course?
The course covers Prompt Engineering, LangChain Components, Chains, Agents, Memory, Tools, RAG, Embeddings, Vector Databases, OpenAI APIs, Google Gemini, Hugging Face integration, LangSmith basics, and AI application deployment.
6. Will I learn Retrieval-Augmented Generation (RAG)?
Yes. You'll build RAG applications using document loaders, embeddings, vector databases, and semantic search.
7. Does the course cover AI Agents?
Yes. You'll build intelligent AI agents capable of using external tools, APIs, search engines, databases, and custom workflows.
8. Which vector databases will I learn?
You'll work with ChromaDB, FAISS, Pinecone concepts, and other popular vector database technologies.
9. Will I learn Prompt Engineering?
Yes. You'll learn prompt design, prompt templates, structured outputs, and prompt optimization for enterprise AI applications.
10. Does the course cover API integration?
Yes. You'll integrate LangChain with OpenAI, Google Gemini, Hugging Face, REST APIs, SQL databases, and external services.
11. What industries use LangChain?
Healthcare, Banking, Insurance, Retail, E-commerce, Education, Manufacturing, Legal Technology, Customer Support, SaaS, and Enterprise AI solutions.
12. What job roles can I apply for after completing this course?
You can apply for Generative AI Engineer, LangChain Developer, AI Engineer, LLM Developer, Prompt Engineer, Machine Learning Engineer, Python AI Developer, NLP Engineer, and AI Consultant roles.
13. Does the course include placement assistance?
Yes. We provide resume building, portfolio development, interview preparation, mock interviews, career guidance, and placement assistance.
14. Are practical labs included?
Yes. Every module includes coding exercises, AI application development, enterprise case studies, and industry-oriented projects.
15. Why choose this LangChain – Building LLM Applications course in Chennai?
Our LangChain training in Chennai offers expert trainers, hands-on AI development, live projects, interview preparation, certification guidance, and placement support to help you build a successful career in Generative AI.
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