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: September 2025