Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Subscribe
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Generative AI: RAG, LangChain, GraphRAG & Fine-Tuning
Hot & New
New
Rating: 5.0 out of 5(1 rating)
5 students

Generative AI: RAG, LangChain, GraphRAG & Fine-Tuning

Build LLM apps with MCP tools, guardrails, memory & Hugging Face deploy
Hot & New
New
Created byDeesa Technologies
Last updated 7/2026
English

What you'll learn

  • Build your first Generative AI applications using LLM APIs (OpenAI, DeepSeek, Groq) and Gradio UIs
  • Understand how LLMs work — tokens, probability, capabilities, limitations, cost, and latency
  • Design effective prompts and tune model parameters like temperature for consistent outputs
  • Evaluate AI model quality using scorecards and LLM-as-judge patterns
  • Apply prompt engineering techniques and implement guardrails for safer, more reliable AI apps
  • Extract structured data from documents (e.g. contract clauses) using LLM-powered pipelines
  • Build LangChain applications with tool integration, web search, and LCEL chains
  • Create an AI SQL Assistant that generates, validates, and runs database queries safely
  • Compare stateless LLMs vs LangChain memory strategies for multi-turn conversations
  • Build Retrieval-Augmented Generation (RAG) systems with vector stores over real documents
  • Use GraphRAG to connect knowledge graphs with vector search for relationship-aware AI
  • Build MCP-powered AI agents that use tools to query data, documents, and external systems
  • Summarize long documents using map-reduce pipelines when content exceeds context limits
  • Fine-tune open-weight models using LoRA and QLoRA on real customer support examples
  • Deploy a fine-tuned model to Hugging Face Spaces with a production-style Gradio demo
  • Structure Gen AI projects the way engineering teams do — config, prompts, services, and guardrails

Course content

13 sections • 50 lectures • 20h 33m total length

  • What is this course about7:05
  • Installation and Project Setup18:32
  • Setting up OpenAI and DeepSeek API Keys28:49
  • Project source code - GitHub Location0:03

Requirements

  • Basic python programming is preferred

Description

Generative AI: RAG, LangChain, GraphRAG & Fine-Tuning is a hands-on course from Deesa Technologies for developers who want to build real Generative AI applications — not just call an API once and stop.

You’ll follow a clear path from your first LLM app through production-style patterns used in modern engineering teams: prompt design, guardrails, structured outputs, LangChain, conversation memory, RAG, GraphRAG, MCP tool integration, map-reduce summarization, and fine-tuning open-weight models with deployment to Hugging Face.

Every module includes runnable projects with a consistent structure (main, services, config, prompts, guardrails) so you learn how professionals organize Gen AI codebases.

What you’ll build

Across 11 modules, you’ll create practical applications including:

  • Executive Brief Generator and Professional Report Drafter — your first Gradio LLM apps

  • Prompt Playground and Temperature & Latency Lab — control model behavior, cost, and speed

  • Model Scorecard and Translation Quality Report — evaluate outputs with real evaluation patterns

  • Output Formatter and Contract Clause Extractor — structured extraction from documents

  • AI SQL Assistant — LangChain, LCEL, and safe natural-language-to-SQL

  • Customer Support Thread Analyzer — stateless vs memory-based conversations

  • On-Call Runbook Assistant — RAG over production runbooks

  • GraphRAG Dependency Explorer — combine graphs + retrieval for relationship-aware answers

  • MCP-powered assistants — connect LLMs to databases, knowledge bases, and tools via Model Context Protocol

  • Long Document Brief Generator — map-reduce summarization for books and large files

  • Customer Support AI — QLoRA fine-tuning on open-weight models + Hugging Face Spaces deployment


Who this course is for:

  • Developers and engineers who want to move from “calling an API” to building real Gen AI applications
  • Python learners comfortable with basic coding who want hands-on AI project experience
  • Software engineers upskilling into AI — backend, data, or full-stack developers adding Gen AI to their toolkit
  • Tech professionals who need practical patterns: prompts, guardrails, RAG, agents, and deployment — not just theory
  • Builders who learn by doing — every module includes runnable projects with production-style code structure
  • Anyone preparing for real-world AI work — support bots, document Q&A, SQL assistants, runbook tools, fine-tuned models
  • Students and career switchers with Python basics who want a clear path from first LLM app to Hugging Face deployment

Report abuse