Mastering Generative AI: LLM Apps, LangChain, RAG & Chatbots

Mastering Generative AI: LLM Apps, LangChain, RAG & Chatbots-Complete Course

Overview

Mastering Generative AI: LLM Apps, LangChain, RAG & Chatbots is a comprehensive, hands-on program designed to help learners build real-world generative AI applications using modern tools such as Large Language Models (LLMs), LangChain, Retrieval-Augmented Generation (RAG), vector databases, and chatbots.

The course follows a practical, end-to-end learning path, starting with Python fundamentals and progressing through NLP, transformers, LLM workflows, and deployment-ready AI systems. It is suitable for learners who want more than theory and are interested in building production-style GenAI applications.

Course Snapshot

• Students Enrolled: 20,800+
• Content Length: ~47 hours
• Skill Level: Beginner to Intermediate
• Language: English (Auto captions available)
• Certification: Certificate of completion included
• Access: Lifetime access (mobile & TV supported)
• Pricing Note: 100% free via coupon (limited time)
• Discount Pattern: 10% OFF coupon released every month-

What This Course Actually Covers

This course provides a complete Generative AI development pipeline, focusing on how modern AI systems are designed, built, and deployed in practice.

Instead of isolated demos, learners work through:

• Python-based AI development
• NLP foundations and transformer models
• LLM-based application architecture
• Retrieval-Augmented Generation (RAG) workflows
• Chatbot and prompt-engineering strategies

The emphasis is on hands-on implementation, not abstract AI theory.

Data Science & AI Masters 2025 – From Python To Gen AI

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Skills & Concepts You’ll Develop

Python & AI Foundations

• Python programming for AI and data workflows
• Working with NumPy and Pandas for data handling
• Preparing data pipelines for AI applications

NLP & Transformer Models

• End-to-end Natural Language Processing pipeline
• Text preprocessing and feature extraction
• Understanding how transformer models power modern NLP tasks

Large Language Models (LLMs)

• Core concepts behind LLMs and generative AI
• Using LLMs for text generation and reasoning tasks
• Designing workflows around ChatGPT-style models

RAG, LangChain & Vector Databases

• Building Retrieval-Augmented Generation (RAG) systems
• Using LangChain to orchestrate LLM workflows
• Storing and retrieving embeddings with vector databases (e.g., FAISS)

Prompt Engineering & Chatbots

• Designing effective prompts for consistent AI outputs
• Building real-world AI chatbots
• Optimizing responses for accuracy and relevance

Who This Course Is Best Suited For

• Beginners exploring Generative AI and LLM applications
• Developers interested in LangChain and RAG architectures
• Data scientists transitioning into applied GenAI
• Professionals building AI-powered chatbots and tools
• Learners looking for project-based AI experience, not just theory

Common Questions Learners Ask

Do I need prior machine learning experience?
Basic ML knowledge is helpful but not mandatory. The course explains concepts step by step.

Is this more theory-based or practical?
Strongly practical. The course focuses on building real AI systems.

Does it cover modern GenAI tools?
Yes. LangChain, RAG, vector databases, prompt engineering, and LLM workflows are core topics.

Is this suitable for real projects?
Yes. The architecture and techniques taught are directly applicable to production-style AI apps.

Practical Value

What sets this course apart is its full-stack GenAI perspective. Learners don’t just use AI models—they understand how data flows, how retrieval improves accuracy, how prompts shape outputs, and how components integrate into scalable AI applications.

Final Thoughts

If you want a single, structured course to understand and build modern generative AI applications—from Python foundations to LLM-powered chatbots and RAG pipelines—this course offers a clear, hands-on starting point. It’s especially valuable for learners aiming to work with real GenAI systems, not just experiments.