llm-roadmap.md

Large Language Models (LLM) Roadmap

This roadmap guides learners from LLM fundamentals to state-of-the-art generative models, retrieval-augmented systems, and production-ready LLM pipelines.
By the end, you should be able to build, fine-tune, evaluate, and deploy LLM-based applications at scale.

A complete Core LLM roadmap — from tokens to intelligent systems.

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Table of Contents

• Prerequisites

• Beginner Level — LLM Foundations

  • Core Concepts

• Intermediate Level — Core LLM Engineering

  • Core Concepts
  • Highly Recommended Resources ⭐

• Advanced Level — Modern & Production LLM Systems

  • Core Concepts (Advanced)

• LLM Architectures & Techniques — Learning Resources

• Projects (Highly Recommended)

  • Beginner Projects
  • Intermediate Projects
  • Advanced Projects

• Final Outcome

• Career Paths After This Roadmap

• Final Notes

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Prerequisites

Before starting LLMs, you should be comfortable with:

• Mathematics

  • Linear Algebra (embeddings, attention)
  • Probability & Statistics 👉 See README.md#the-math-behind-it-all

• Programming

  • Python
  • PyTorch basics

• Deep Learning & NLP Fundamentals

  • Neural Networks
  • Sequence Models
  • Tokenization

• Tools

  • Hugging Face Transformers
  • CUDA basics (optional but useful)

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Beginner Level — LLM Foundations

Understand how large language models work internally.

Core Concepts

• Language Modeling
• Tokenization (BPE, WordPiece)
• Embeddings
• Attention Mechanism
• Autoregressive vs Masked Models

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S.No	Best Book	Best YouTube Playlist	Best University Course	Level
1	Speech and Language Processing – Jurafsky	3Blue1Brown – Attention	Stanford CS224n	Beginner
2	Natural Language Processing with Transformers	Hugging Face Course	Oxford NLP	Beginner

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Intermediate Level — Core LLM Engineering

Learn to fine-tune, adapt, and evaluate LLMs.

Core Concepts

• Transformer Architecture
• Pretraining vs Fine-Tuning
• Parameter-Efficient Fine-Tuning (LoRA, PEFT)
• Instruction Tuning
• Evaluation Metrics (Perplexity, BLEU, ROUGE)

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S.No	Best Book	Best YouTube Playlist	Best University Course	Level
1	Natural Language Processing with Transformers	CS224n – Stanford	Stanford CS224n	Intermediate
2	Designing Machine Learning Systems – Chip Huyen	Hugging Face Transformers	Full Stack Deep Learning	Intermediate
3	Deep Learning – Goodfellow	AssemblyAI – LLMs	MIT 6.S191	Intermediate

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Highly Recommended Resources ⭐

• Hugging Face LLM Course https://huggingface.co/course

• Stanford CS224n https://web.stanford.edu/class/cs224n/

• Attention Is All You Need (Paper) https://arxiv.org/abs/1706.03762

• PEFT / LoRA Documentation https://huggingface.co/docs/peft/index

❗ Hugging Face + CS224n together form the strongest practical LLM foundation available today.

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Advanced Level — Modern & Production LLM Systems

Build scalable, safe, and efficient LLM-powered systems.

Core Concepts

• Large-Scale Pretraining
• RLHF
• Retrieval-Augmented Generation (RAG)
• Inference Optimization (Quantization, KV Cache)
• LLM System Design

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S.No	Best Book	Best YouTube Playlist	Best University Course	Level
1	Designing Machine Learning Systems – Chip Huyen	Full Stack Deep Learning	Stanford CS25	Advanced
2	Generative AI with LLMs	Hugging Face Advanced	MIT LLM Systems	Advanced

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LLM Architectures & Techniques — Learning Resources

S.No	Architecture / Technique	Best Video	Best Blog / Article	Level
1	Transformer	Illustrated Transformer	Jay Alammar	Beginner
2	GPT-style Models	GPT Explained	OpenAI GPT Paper	Intermediate
3	BERT-style Models	BERT Explained	BERT Paper	Intermediate
4	Instruction Tuning	Instruction Tuning	FLAN Paper	Intermediate
5	RLHF	RLHF Explained	InstructGPT	Advanced
6	RAG Systems	RAG Explained	Pinecone Guide	Advanced

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Projects (Highly Recommended)

Projects are mandatory to truly master LLMs.

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Beginner Projects

• Prompt-Based Text Generator
  Skills: Prompting, Tokenization

• Text Classification with BERT Skills: Fine-Tuning, Transformers

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Intermediate Projects

• LLM Fine-Tuning (LoRA) Skills: PEFT, Efficient Training

• Document Q&A System Skills: Embeddings, Vector Search

• Chatbot with Memory Skills: Context Management

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Advanced Projects

• RAG-Based Production Chatbot Skills: Retrieval, LLM Orchestration

• Inference-Optimized LLM Deployment Skills: Quantization, Serving

• End-to-End LLM System Design Skills: Scaling, Monitoring, Safety

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Final Outcome

By completing this roadmap, you will be able to:

• Understand how LLMs work internally
• Fine-tune and adapt LLMs efficiently
• Build production-grade GenAI systems
• Evaluate and optimize LLM performance

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Career Paths After This Roadmap

• LLM Engineer
• Generative AI Engineer
• Applied Research Engineer
• AI Systems Engineer

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Final Notes

You don’t need to train trillion-parameter models.
Understanding + adaptation + system design = real LLM expertise.