LLM Development Roadmap

A hands-on roadmap to master LLM development from neural networks to production deployment. Build, train, and deploy language models with practical implementations and real-world examples.

Roadmap Overview

Part Key Topics Time Estimate
🔍 Part 1: The Foundations Neural Networks, Transformers, Tokenization, Embeddings, Attention Mechanisms 12-20 weeks
🧬 Part 2: Building & Training Data Preparation, Pre-training, Fine-tuning, RLHF, Preference Alignment 16-28 weeks
⚙️ Part 3: Advanced Topics Model Evaluation, Reasoning, Quantization, Inference Optimization, Architectures 20-36 weeks
🚀 Part 4: Engineering & Apps Production Deployment, RAG, Agents, Multimodal, Security, LLMOps 12-24 weeks
💡 Total Time Commitment: 12-24 months

📋 Getting Started

🌱 Complete Beginner

No ML experience

🚀 Quick Start

Some programming experience

  • Review Python if needed
  • Jump to Part 1: Neural Networks

⚡ Advanced

ML/AI experience

  • Skip to Part 2 or area of interest
  • Use Part 1 as reference

🔧 LLM Engineering

Build apps fast

  • Prerequisites: Python, basic APIs
  • Path: Part 1 → Part 4
  • Outcome: Production LLM apps

Prerequisites

Programming & Dev

Mathematics

Machine Learning

Part 1: The Foundations 🔍

Core ML concepts, neural networks, traditional models, tokenization, embeddings, transformers

Part 2: Building & Training Models 🧬

Data preparation, pre-training, fine-tuning, preference alignment

Part 3: Advanced Topics & Specialization ⚙️

Evaluation, reasoning, optimization, architectures, enhancement

Part 4: Engineering & Applications 🚀

Production deployment, RAG, agents, multimodal, security, ops