Roadmap to Mastery: Designing & Building Large Language Models (LLMs)
Goal: Become an expert capable of designing, training, aligning, deploying, and scaling modern LLMs (GPT-class), and founding an LLM-focused company.
Already knowing NN basics, CNNs, LSTMs, training loops, and DS fundamentals. This roadmap starts at Transformers and goes all the way to production-grade GPTs + RLHF.
PHASE 0 — Foundations You Must Fully Internalize (Short but Deep)
Achievement: You can derive everything in a Transformer with pen & paper.
Math (Notebook-only mastery)
- Linear algebra (deep level)
- Probability & information theory
- Optimization
- Numerical stability
Resources
- Stanford CS229 notes (math-heavy)
- “The Matrix Calculus You Need For Deep Learning” – Terence Parr
- Deep Learning Book (Goodfellow) – Ch. 6–8
PHASE 1 — Transformer Architecture (Absolute Core)
Achievement: You can implement GPT-2 from scratch without copying code.
Topics
- Self-Attention
- Multi-Head Attention
- Positional encodings
- Transformer blocks
- Feedforward networks (MLP blocks)
- Residual connections (signal propagation)
Hands-on
- Implement:
Resources
- “Attention Is All You Need” (paper)
- The Annotated Transformer
- Andrej Karpathy – “Let’s build GPT from scratch”
- nanoGPT (GitHub)
PHASE 2 — Language Modeling at Scale
Achievement: You understand why GPT works better as it scales.
Topics
- Autoregressive language modeling
- Tokenization
- Vocabulary design
- Embeddings
- Loss functions
- Sampling
Hands-on
- Train a GPT on:
- Write your own tokenizer
- Compare sampling strategies
Resources
- HuggingFace Tokenizers docs
- OpenAI GPT-2 paper
- Jay Alammar – GPT visualizations
PHASE 3 — Scaling Laws & Training Large Models
Achievement: You can predict model performance before training.
Topics
- Scaling laws
- Batch size vs learning rate
- Gradient accumulation
- Mixed precision (FP16, BF16)
- Initialization schemes
- Training instabilities
Hands-on
- Implement:
- Reproduce small-scale scaling law experiments
Resources
- “Scaling Laws for Neural Language Models”
- DeepMind Chinchilla paper
- EleutherAI scaling notes
PHASE 4 — Modern GPT Improvements
Achievement: Your model matches modern architectural standards.
Topics
- Rotary Position Embeddings (RoPE)
- ALiBi
- RMSNorm
- SwiGLU / GeGLU
- FlashAttention
- KV Caching
- Long-context techniques
Hands-on
- Modify GPT:
Resources
- FlashAttention paper & repo
- LLaMA architecture breakdown
- HuggingFace Transformers source code
PHASE 5 — Data Engineering for LLMs
Achievement: You can build high-quality datasets at scale.
Topics
- Web-scale data collection
- Deduplication
- Filtering (quality, toxicity)
- Instruction data
- Synthetic data generation
- Data mixtures
Hands-on
- Build:
Resources
- The Pile paper
- RedPajama dataset
- OpenAI data filtering discussions
PHASE 6 — Distributed & Large-Scale Training
Achievement: You can train billion-parameter models.
Topics
- Data parallelism
- Model parallelism
- Pipeline parallelism
- ZeRO (DeepSpeed)
- FSDP (PyTorch)
- Checkpointing & fault tolerance
Hands-on
- Train GPT with:
- Multi-node training
Resources
- DeepSpeed docs
- PyTorch FSDP tutorials
- Megatron-LM
PHASE 7 — Alignment & Reinforcement Learning (RLHF)
Achievement: You can align models like OpenAI/DeepSeek.
Topics
- Instruction tuning
- Supervised Fine-Tuning (SFT)
- Reward models
- Reinforcement Learning from Human Feedback
- Preference datasets
Hands-on
- Train:
Resources
- InstructGPT paper
- DeepSeek-RL paper
- HuggingFace TRL
- Anthropic Constitutional AI paper
PHASE 8 — Inference Optimization & Deployment
Achievement: You can serve LLMs at production scale.
Topics
- Quantization
- Inference engines
- KV cache optimization
- Batching & latency tradeoffs
Hands-on
- Deploy:
- Benchmark latency & throughput
Resources
- vLLM docs
- NVIDIA TensorRT-LLM
- OpenAI inference optimization talks
PHASE 9 — Safety, Evaluation & Red Teaming
Achievement: Your model is production-safe.
Topics
- Evaluation benchmarks
- Hallucinations
- Bias & fairness
- Jailbreak resistance
- Model monitoring
Hands-on
- Build eval harness
- Red-team your model
Resources
- HELM benchmark
- OpenAI safety papers
- Anthropic safety research
PHASE 10 — From LLM to Company
Achievement: You can launch an LLM startup.
Topics
- Model licensing
- Open vs closed models
- Cost modeling
- Fine-tuning as a service
- Vertical LLMs
- API design
- Moats in LLM startups
Resources
- a16z LLM market analysis
- OpenAI API docs
- YC LLM startup talks
FINAL CAPSTONE
Build from scratch:
- Tokenizer
- GPT architecture
- Distributed training
- Instruction tuning
- RLHF
- Inference engine
- Public API
🎯 Outcome: You are capable of designing GPT-class models and founding an LLM company.
If you want, I can next:
- Convert this into a 12–18 month study plan
- Give weekly milestones
- Or give a “build-your-own GPT” repo structure