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, featuring the latest LangChain v1.x architecture, agentic AI patterns, and production-ready GenAI systems.

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What's New in 2026

This roadmap has been updated for January 2026 to reflect the latest advancements in Generative AI and LangChain ecosystem.

Key Updates:

• LangChain v1.x Series – Production-ready agent framework with create_agent, middleware, and LangGraph 1.0
• Latest LLMs – GPT-5. 2, Claude Opus 4.5, Gemini 3 Pro comparisons and integration
• DeepAgents & LangGraph – Long-running autonomous agent workflows
• Enterprise Features – LangSmith v0.13, observability, cost tracking, and security
• Advanced RAG – Multi-agent RAG, multimodal retrieval, and dynamic knowledge updating
• New Courses & Certifications – LangChain Academy certifications, updated DeepLearning.AI courses

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Table of Contents

• Overview
• 2026 GenAI Landscape
• 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 covering the complete GenAI stack from foundations to production deployment.

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2026 GenAI Landscape

Current State of Large Language Models (January 2026)

Model	Provider	Context Window	Key Strengths	Best For
GPT-5.2 Pro	OpenAI	400,000 tokens	Reasoning, low hallucination (~1%), coding	Enterprise, R&D, complex tasks
Claude Opus 4.5	Anthropic	~200,000 tokens	Safety, thoughtful outputs, SWE-Bench leader	Research, code fixing, document analysis
Gemini 3 Pro	Google	1,000,000 tokens	Multimodal, fastest generation, largest context	Cross-format tasks, real-time apps
Llama 3.2	Meta	128,000 tokens	Open-source, customizable	Self-hosted, fine-tuning
DeepSeek-V3	DeepSeek	128,000 tokens	Cost-effective, strong reasoning	Budget-conscious deployments

Key GenAI Trends in 2026

1. Agentic AI Revolution – AI agents now autonomously manage workflows, interact with tools, and orchestrate multi-step processes
2. Multimodal by Default – Models seamlessly process text, images, video, audio, and code
3. Smaller, Greener Models – Quantization and pruning enable efficient edge deployment
4. AI-Native Enterprises – GenAI embedded in core business operations, not just experiments
5. Ethics as Engineering – Responsible AI practices built directly into development pipelines

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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 (Word2Vec, GloVe, FastText)
  • Modern Tokenization (BPE, SentencePiece)

• Deep Learning for NLP

  • RNNs, LSTMs, and GRUs
  • Attention Mechanisms
  • Transformers Architecture (The Foundation of Modern AI)

2. Generative AI Models (4-6 weeks)

• Transformer-Based Models

  • GPT Family (GPT-4, GPT-5, GPT-5.2)
  • Claude Series (Sonnet, Opus)
  • Gemini Family (Pro, Flash, Ultra)
  • Open-Source: Llama 3.x, Mistral, DeepSeek

• Multimodal Models

  • Vision-Language Models (GPT-5V, Gemini Vision)
  • Image Generation (DALL-E 3, Stable Diffusion 3, Midjourney v6)
  • Video Generation (Sora, Runway Gen-3)
  • Audio Models (Whisper, ElevenLabs)

• Fine-tuning Strategies

  • Transfer Learning
  • Prompt Engineering & Optimization
  • PEFT (LoRA, QLoRA, Prefix Tuning)
  • RLHF & DPO (Direct Preference Optimization)
  • Distillation & Quantization

3. LangChain Mastery (4-6 weeks)

Updated for LangChain v1.x (January 2026)

• LangChain v1.x Basics

  • New create_agent Architecture
  • Components and Unified Namespace
  • Chains, Agents, and Memory Types
  • LangChain Expression Language (LCEL)

• Middleware & Guardrails

  • PIIMiddleware (Data Redaction)
  • SummarizationMiddleware (Context Management)
  • HumanInTheLoopMiddleware (Approval Workflows)
  • Content Moderation Middleware
  • Model Retry with Exponential Backoff

• Prompt Engineering with LangChain

  • Template Creation & Management
  • Few-shot and Zero-shot Learning
  • Chain of Thought Prompting
  • Dynamic Prompt Construction

• Advanced LangChain Features

  • Document Loading and Intelligent Splitting
  • Vector Stores (Chroma, Pinecone, Weaviate, FAISS)
  • Embeddings (OpenAI, Cohere, HuggingFace)
  • Retrieval Augmented Generation (RAG)
  • Tool and API Integration
  • MCP Adapters for Multimodal Tools

4. LangGraph & Agentic AI (3-4 weeks)

New Section for 2026

• LangGraph 1.0 Fundamentals

  • State Machines for AI Workflows
  • Multi-Agent Orchestration
  • Conditional Branching & Loops
  • Human-in-the-Loop Patterns

• DeepAgents

  • Long-running Autonomous Workflows
  • Pluggable Storage Backends (S3, Cloud)
  • Remote Sandboxes for Security
  • Composite Agent Architectures

• Building Production Agents

  • Task Decomposition Patterns
  • Tool Selection & Execution
  • Error Recovery & Fallbacks
  • Agent Memory & State Persistence

5. Advanced RAG Systems (3-4 weeks)

Updated RAG Best Practices for 2026

• RAG Architecture Design

  • Chunking Strategies (256-512 tokens optimal)
  • Embedding Model Selection
  • Hybrid Search (Vector + Keyword)
  • Re-ranking for Precision

• Production RAG Patterns

  • Multi-step RAG Pipelines
  • Context Condensation
  • Source Citation & Traceability
  • Dynamic Knowledge Updating

• Multimodal RAG

  • Image & Document Understanding
  • Table Extraction & Processing
  • Cross-Modal Retrieval

6. Applied GenAI Projects (4-8 weeks)

• Building Conversational Agents

  • Chatbots with Memory & Context
  • Multi-turn Dialogue Management
  • Task-specific Agentic Systems

• Content Generation Systems

  • Text Summarization
  • Creative Writing Assistants
  • Code Generation & Review
  • Report Generation

• Information Retrieval & Knowledge Systems

  • Enterprise Q&A Systems
  • Knowledge Base Construction
  • Document Analysis Pipelines
  • Semantic Search Engines

7. Production & Deployment (2-4 weeks)

• Model Optimization

  • Quantization (INT8, INT4)
  • Distillation
  • Inference Optimization (vLLM, TensorRT)

• Deployment Strategies

  • API Development (FastAPI, LangServe)
  • Containerization (Docker, Kubernetes)
  • Serverless Deployment (AWS Lambda, Cloud Functions)
  • Edge Deployment

• Observability with LangSmith

  • Trace Debugging & Analysis
  • Performance Monitoring
  • Cost Tracking & Optimization
  • A/B Testing with Pairwise Annotation Queues
  • LangSmith Fetch CLI for IDE Debugging

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LangChain Integration

LangChain v1.x provides a production-ready framework for developing applications powered by language models. This repository demonstrates how to leverage LangChain for:

• Building Complex Reasoning Chains with LCEL
• Creating Domain-Specific Chatbots with custom memory
• Implementing Production RAG Systems with hybrid retrieval
• Developing Autonomous Agents with LangGraph
• Connecting LLMs to External Tools and APIs via MCP Adapters
• Deploying Secure, Observable AI Systems with LangSmith

LangChain v1.x Key Features (2026)

# New create_agent API (LangChain v1.x)
from langchain import create_agent
from langchain.middleware import (
    PIIMiddleware,
    SummarizationMiddleware,
    HumanInTheLoopMiddleware
)

agent = create_agent(
    model="gpt-5.2",
    tools=[search_tool, calculator_tool],
    middleware=[
        PIIMiddleware(),
        SummarizationMiddleware(max_tokens=4000),
        HumanInTheLoopMiddleware(require_approval=["sensitive_action"])
    ]
)

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Top Resources

Official Documentation

• LangChain Documentation
• LangChain GitHub Repository
• LangChain v1.x Migration Guide
• LangGraph Documentation
• LangSmith Platform
• LangChain Changelog (2026)

Courses & Certifications (2026)

Course	Platform	Level	Certificate
LangChain for LLM Application Development	DeepLearning.AI	Beginner	✅
LangChain Academy - Foundation Track	LangChain	Beginner-Advanced	✅ Official
LangChain Academy - DeepAgents Track	LangChain	Advanced	✅ Official
Agentic AI with LangChain and LangGraph	IBM/Coursera	Intermediate	✅
Developing LLM Applications with LangChain	DataCamp	Intermediate	✅
LangChain Mastery Course	Udemy	All Levels	✅
Free LangChain Basics	Great Learning	Beginner	✅ Free

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" (2nd Edition) by David Foster
• "Transformers for Natural Language Processing" by Denis Rothman
• "LangChain in Action" by Harrison Chase (2025)
• "Designing Large Language Model Applications" by O'Reilly (2025)

Tutorials and Articles

• LangChain Cookbook
• Building LLM Applications for Production by Chip Huyen
• Prompt Engineering Guide
• RAG Application Development Guide 2026
• The Illustrated Transformer
• LangChain Best Practices (2026)

YouTube Channels

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

Research Papers (Essential Reading)

• 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
• Engineering the RAG Stack (2026) - Latest RAG architecture review
• Direct Preference Optimization - DPO paper

Tools & Platforms

• LangSmith - Observability & debugging
• Pinecone - Vector database
• ChromaDB - Open-source embeddings database
• Weaviate - Vector search engine
• vLLM - High-throughput inference

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Getting Started

Prerequisites

• Python 3.10 or higher (recommended: 3.11+)
• pip (Python package manager)
• OpenAI API key (or other LLM provider keys)

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
cat > .env << EOF
OPENAI_API_KEY=your_openai_api_key
ANTHROPIC_API_KEY=your_anthropic_api_key
LANGCHAIN_API_KEY=your_langsmith_api_key
LANGCHAIN_TRACING_V2=true
EOF

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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 v1.x
│   ├── components/            # Core components of LangChain
│   ├── chains/                # Building and using chains
│   ├── memory/                # Working with different memory types
│   └── middleware/            # NEW: Middleware patterns
├── langchain_advanced/        # Advanced LangChain implementations
│   ├── lcel/                  # LangChain Expression Language
│   ├── rag/                   # Retrieval Augmented Generation
│   ├── agents/                # Building autonomous agents
│   └── tools/                 # Tool integration
├── langgraph/                 # NEW: LangGraph tutorials
│   ├── basics/                # State machines & workflows
│   ├── multi_agent/           # Multi-agent orchestration
│   └── deep_agents/           # Long-running agent patterns
├── projects/                  # Complete project implementations
│   ├── chatbot/               # Conversational agent examples
│   ├── document_qa/           # Document Q&A system
│   ├── content_generator/     # Text generation applications
│   └── agentic_assistant/     # NEW: Autonomous agent project
├── deployment/                # Deployment guides and examples
│   ├── langserve/             # LangServe API deployment
│   ├── langsmith/             # Observability setup
│   ├── optimization/          # Model optimization techniques
│   └── monitoring/            # Production monitoring
├── resources/                 # Additional learning resources
├── notebooks/                 # Jupyter notebooks with examples
├── requirements.txt           # Project dependencies
├── . env. example               # Example environment variables
└── README.md                  # This documentation

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Examples

Basic LangChain Chain (v1.x)

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

# Initialize the LLM
llm = ChatOpenAI(model="gpt-5.2", temperature=0.7)

# Create a prompt template
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful AI assistant."),
    ("user", "Write a short paragraph about {topic}.")
])

# Create a chain using LCEL
chain = prompt | llm | StrOutputParser()

# Run the chain
result = chain.invoke({"topic":  "artificial intelligence in 2026"})
print(result)

Production RAG Implementation (2026)

from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community. document_loaders import TextLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_chroma import Chroma
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough

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

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=512,  # Optimal chunk size for 2026
    chunk_overlap=50
)
texts = text_splitter.split_documents(documents)

# Create embeddings and vector store
embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
db = Chroma.from_documents(texts, embeddings)
retriever = db.as_retriever(search_kwargs={"k": 5})

# Create RAG chain with source citation
prompt = ChatPromptTemplate. from_template("""
Answer the question based on the following context.  
Always cite your sources. 

Context: {context}

Question:  {question}

Answer:""")

llm = ChatOpenAI(model="gpt-5.2")

rag_chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | llm
)

# Query with traceability
response = rag_chain.invoke("What are the key points? ")
print(response.content)

LangGraph Agent (New in 2026)

from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool

@tool
def search_web(query: str) -> str:
    """Search the web for information."""
    # Implement web search
    return f"Search results for:  {query}"

@tool
def calculate(expression: str) -> str:
    """Evaluate a mathematical expression."""
    return str(eval(expression))

# Create agent with LangGraph
llm = ChatOpenAI(model="gpt-5.2")
agent = create_react_agent(llm, [search_web, calculate])

# Run agent with state management
result = agent.invoke({
    "messages": [("user", "What is 25 * 4 and who invented calculus?")]
})
print(result["messages"][-1]. content)

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

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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.

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License

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

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Contact

• Website: Adil Shamim
• GitHub: Adil Shamim
• Create an issue in this repository for questions or suggestions

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