GenAI Roadmap with Notes Using LangChain

A comprehensive roadmap and resource collection for learning Generative AI with practical implementation using LangChain. This repository serves as a guided journey from basic concepts to advanced applications in the generative AI space.

📋 Table of Contents

• Overview
• GenAI Roadmap
• LangChain Integration
• Top Resources
• Getting Started
• Project Structure
• Examples
• Contributing
• License

🔍 Overview

This repository provides a structured learning path for developers interested in Generative AI with a focus on practical implementation using LangChain. It contains curated notes, code examples, and implementation guides to help you progress from foundational concepts to building sophisticated GenAI applications.

🛣️ GenAI Roadmap

1. Foundations (2-4 weeks)

• Machine Learning Basics

  • Supervised vs. Unsupervised Learning
  • Neural Networks Fundamentals
  • Training and Evaluation Metrics

• NLP Fundamentals

  • Text Processing Techniques
  • Word Embeddings
  • Language Models Basics

• Deep Learning for NLP

  • RNNs, LSTMs, and GRUs
  • Attention Mechanisms
  • Transformers Architecture

2. Generative AI Models (4-6 weeks)

• Transformer-Based Models

  • BERT, GPT Family (GPT-2, GPT-3, GPT-4)
  • T5, BART

• Multimodal Models

  • CLIP, DALL-E
  • Stable Diffusion
  • Multimodal Transformers

• Fine-tuning Strategies

  • Transfer Learning
  • Prompt Engineering
  • PEFT (Parameter-Efficient Fine-Tuning)
  • RLHF (Reinforcement Learning from Human Feedback)

3. LangChain Mastery (3-5 weeks)

• LangChain Basics

  • Components and Architecture
  • Chains and Agents
  • Memory Types

• Prompt Engineering with LangChain

  • Template Creation
  • Few-shot Learning
  • Chain of Thought Prompting

• Advanced LangChain Features

  • Document Loading and Splitting
  • Vector Stores and Embeddings
  • Retrieval Augmented Generation (RAG)
  • Tool and API Integration

4. Applied GenAI Projects (4-8 weeks)

• Building Conversational Agents

  • Chatbots and Virtual Assistants
  • Task-specific Agents

• Content Generation Systems

  • Text Summarization
  • Creative Writing Assistants
  • Code Generation

• Information Retrieval & Knowledge Systems

  • Question Answering
  • Knowledge Base Construction
  • Document Analysis

5. Production and Deployment (2-4 weeks)

• Model Optimization

  • Quantization
  • Distillation
  • Inference Optimization

• Deployment Strategies

  • API Development
  • Containerization
  • Serverless Deployment

• Monitoring and Maintenance

  • Performance Metrics
  • Drift Detection
  • Continuous Improvement

🔗 LangChain Integration

LangChain provides a framework for developing applications powered by language models. This repository demonstrates how to leverage LangChain for:

• Building Complex Reasoning Chains
• Creating Domain-Specific Chatbots
• Implementing Retrieval-Augmented Generation (RAG)
• Developing Autonomous Agents
• Connecting LLMs to External Tools and APIs

📚 Top Resources

Official Documentation

• LangChain Documentation
• LangChain GitHub Repository
• LangChain Python API Reference

Books

• "Building LLM Powered Applications" by Simon Willison
• "Natural Language Processing with Transformers" by Lewis Tunstall, Leandro von Werra, and Thomas Wolf
• "Generative Deep Learning" by David Foster
• "Transformers for Natural Language Processing" by Denis Rothman

Courses

• DeepLearning.AI - LangChain for LLM Application Development
• DeepLearning.AI - Building Systems with the ChatGPT API
• Coursera - Generative AI with Large Language Models
• Udemy - LangChain: Create LLM-Powered Applications

Tutorials and Articles

• LangChain Cookbook
• Building LLM Applications for Production by Chip Huyen
• Prompt Engineering Guide
• LangChain for Beginners
• The Illustrated Transformer

YouTube Channels

• LangChain
• DeepLearning.AI
• Weights & Biases
• AI Coffee Break with Letitia

Research Papers

• Attention Is All You Need - Transformer architecture
• Language Models are Few-Shot Learners - GPT-3 paper
• Training Language Models to Follow Instructions with Human Feedback - InstructGPT/RLHF
• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks - RAG paper

🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• pip (Python package manager)

Installation

1. Clone this repository:

git clone https://github.com/AdilShamim8/GenAI-Roadmap-with-Notes-Using-LangChain.git
cd GenAI-Roadmap-with-Notes-Using-LangChain

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install required packages:

pip install -r requirements.txt

4. Set up environment variables:

# Create a .env file with your API keys
echo "OPENAI_API_KEY=your_openai_api_key" > .env

📂 Project Structure

GenAI-Roadmap-with-Notes-Using-LangChain/
├── foundations/                # Basic concepts and foundational knowledge
│   ├── nlp_basics/            # NLP fundamentals
│   ├── transformers/          # Transformer architecture notes
│   └── llm_concepts/          # LLM theory and concepts
├── langchain_basics/          # Introduction to LangChain
│   ├── components/            # Core components of LangChain
│   ├── chains/                # Building and using chains
│   └── memory/                # Working with different memory types
├── advanced_techniques/       # Advanced LangChain implementations
│   ├── rag/                   # Retrieval Augmented Generation
│   ├── agents/                # Building autonomous agents
│   └── fine_tuning/           # Fine-tuning techniques
├── projects/                  # Complete project implementations
│   ├── chatbot/               # Conversational agent examples
│   ├── document_qa/           # Document Q&A system
│   └── content_generator/     # Text generation applications
├── deployment/                # Deployment guides and examples
│   ├── api_setup/             # Setting up APIs
│   ├── optimization/          # Model optimization techniques
│   └── monitoring/            # System monitoring
├── resources/                 # Additional learning resources
├── notebooks/                 # Jupyter notebooks with examples
├── requirements.txt           # Project dependencies
├── .env.example               # Example environment variables
└── README.md                  # This documentation

🧪 Examples

Basic LangChain Chain

from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

# Initialize the LLM
llm = OpenAI(temperature=0.7)

# Create a prompt template
prompt = PromptTemplate(
    input_variables=["topic"],
    template="Write a short paragraph about {topic}."
)

# Create a chain
chain = LLMChain(llm=llm, prompt=prompt)

# Run the chain
result = chain.run("artificial intelligence")
print(result)

Simple RAG Implementation

from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI

# Load document
loader = TextLoader("path/to/document.txt")
documents = loader.load()

# Split text into chunks
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)

# Create embeddings and vector store
embeddings = OpenAIEmbeddings()
db = Chroma.from_documents(texts, embeddings)

# Create a retrieval chain
qa_chain = RetrievalQA.from_chain_type(
    llm=OpenAI(),
    chain_type="stuff",
    retriever=db.as_retriever()
)

# Query the system
query = "What are the key points in this document?"
response = qa_chain.run(query)
print(response)

Check the notebooks/ directory for more complete examples and tutorials.

👥 Contributing

Contributions are welcome! If you'd like to add to this roadmap, improve existing content, or share your implementations:

1. Fork the repository
2. Create a new branch (git checkout -b feature/your-feature)
3. Commit your changes (git commit -m 'Add some feature')
4. Push to the branch (git push origin feature/your-feature)
5. Open a Pull Request

Please see CONTRIBUTING.md for detailed guidelines.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Made with ❤️ by Adil Shamim

Last updated: July 2025