Generative AI Learning Journey 🚀

My journey of learning Generative AI from scratch — documenting concepts, hands-on practice, and key learnings along the way.

The goal is to build a strong foundation in Generative AI and gradually develop real-world AI-powered applications using JavaScript and modern AI tools.

📚 Learning Roadmap

1. Gen AI Fundamentals

• [✔] Introduction to Artificial Intelligence (AI)
• [✔] Machine Learning vs Deep Learning vs Generative AI
• [✔] What are Large Language Models (LLMs)?
• [✔] How LLMs work
• [✔] Tokens and Tokenization
• [✔] Embeddings and Vector Representations
• [✔] Transformer Architecture
• [✔] Attention Mechanism
• [✔] Next-Token Prediction
• [✔] Model Training vs Inference
• [✔] Parameters and Context Window
• [✔] Temperature and Sampling

2. Prompt Engineering

• Understanding Prompts and Instructions
• Zero-Shot and Few-Shot Prompting
• System, User, and Assistant Messages
• Role-Based Prompting
• Structured Outputs (JSON)
• Prompt Templates
• Prompt Evaluation and Optimization

3. LLM Providers and APIs

• Understanding LLM Providers
• Working with Groq API
• Working with OpenAI API
• Working with Google Gemini API
• Using LLMs with JavaScript
• Environment Variables and API Key Security
• Streaming Responses
• Error Handling and Rate Limits

4. LangChain

• [✔] Introduction to LangChain
• [✔] Connecting LLMs with LangChain
• [✔] Chat Models and Messages
• [✔] Prompt Templates
• [✔] Output Parsers
• Structured Output Generation
• Chains and LCEL
• Tool Calling
• Agents and Agent Workflows

5. Embeddings and Vector Databases

• Understanding Embeddings
• Similarity Search
• Cosine Similarity
• Introduction to Vector Databases
• Working with a Vector Database
• Semantic Search

6. Retrieval-Augmented Generation (RAG)

• What is RAG?
• RAG Architecture and Workflow
• Document Loading
• Text Splitting and Chunking
• Generating Document Embeddings
• Storing and Retrieving Documents
• Building a RAG Pipeline
• Chatting with PDFs and Documents
• RAG Evaluation and Optimization

7. AI Agents and Tool Use

• Understanding AI Agents
• Function and Tool Calling
• Connecting LLMs to External APIs
• Agent Planning and Execution
• Memory in AI Applications
• Multi-Step Workflows
• Human-in-the-Loop Workflows

8. Building AI Applications

• Build a Basic AI Chatbot
• Build a Streaming Chat Interface
• Build a Document Q&A Application
• Build a RAG-Based Chatbot
• Integrate LLMs with Node.js and Express
• Build a Full-Stack AI Application
• Deploy an AI Application

9. Production-Ready Gen AI

• LLM Evaluation and Testing
• Hallucinations and Their Limitations
• Guardrails and Input Validation
• Responsible AI and Safety
• Logging and Monitoring
• Latency and Cost Optimization
• Rate Limiting and Security
• Introduction to LLM Observability

🎯 Learning Goals

• Build a strong understanding of Generative AI fundamentals.
• Learn to integrate LLMs into real-world applications.
• Develop practical skills with LLM APIs, LangChain, RAG, and AI agents.
• Build and deploy useful AI-powered projects.
• Document my learning and continuously improve through hands-on practice.

📈 Progress

Learning one concept at a time, writing code, experimenting, and building projects along the way.

Consistency over perfection. Learning by building. 💻