Master Large Language Models
A production-grade curriculum for engineers who want to understand LLMs from the inside out.
Most LLM courses teach you to write a prompt. This curriculum teaches you what happens after you press enter - from attention heads to RLHF, from tokenizers to trillion-parameter serving.
The Curriculum
17 modules. The deepest LLM engineering curriculum available - from architecture to alignment.
Architecture and Training
Who it's for: Engineers who want to understand how LLMs actually work, not just how to call them.
| Module | Topics |
|---|---|
| 01 - Transformer Architecture | Attention mechanism, positional encoding, multi-head attention, layer norms, scaling |
| 02 - Pretraining and Fine-Tuning | Language modeling objectives, tokenization, LoRA, QLoRA, PEFT, data mixing |
| 03 - Prompt Engineering | Zero-shot, few-shot, chain-of-thought, prompt testing, template management |
RAG, Agents, and Evaluation
Who it's for: Engineers building LLM-powered applications with retrieval and tool use.
| Module | Topics |
|---|---|
| 04 - RAG Systems | When to use RAG, chunking, retrieval, reranking, hybrid search, evaluation |
| 05 - LLM Agents | Tool use, function calling, ReAct, planning, agent loops, error recovery |
| 06 - LLM Evaluation | Perplexity, benchmarks, LLM-as-judge, human eval, red teaming, regression testing |
Inference and Optimization
Who it's for: Engineers optimizing LLM serving for latency, throughput, and cost.
| Module | Topics |
|---|---|
| 07 - LLM Inference and Optimization | Autoregressive decoding, KV cache, speculative decoding, batching, vLLM |
| 08 - Multimodal Models | Vision-language models, image understanding, audio, video, cross-modal attention |
| 09 - LLM System Design | Product architecture, context windows, caching, routing, cost management |
Advanced Architectures
Who it's for: Engineers tracking the frontier of LLM research and architecture innovations.
| Module | Topics |
|---|---|
| 10 - Reasoning Models | Test-time compute, chain-of-thought distillation, self-consistency, verification |
| 11 - Mixture of Experts | MoE architecture, routing, load balancing, sparse activation, Switch Transformer |
| 12 - State Space Models | Limitations of attention, Mamba, S4, linear-time sequence modeling |
| 13 - Structured Generation | Constrained decoding, JSON mode, grammar-guided generation, Outlines |
Advanced Techniques
Who it's for: Senior engineers working with model merging, long context, embeddings, and alignment.
| Module | Topics |
|---|---|
| 14 - Model Merging | SLERP, TIES, DARE, task arithmetic, merging strategies, evaluation |
| 15 - Long Context Strategies | Attention at long contexts, RoPE scaling, retrieval augmentation, context compression |
| 16 - Alignment and Safety | The alignment problem, RLHF, DPO, constitutional AI, red teaming, guardrails |
| 17 - Embeddings Engineering | Embedding models, contrastive learning, sentence transformers, fine-tuning, evaluation |
What You Will Be Able to Do
After completing this curriculum:
- Explain transformer architecture from first principles - attention, positional encoding, and all
- Build and evaluate RAG systems that retrieve the right context, not just the nearest vector
- Optimize LLM inference with KV caching, speculative decoding, and batching strategies
- Fine-tune models with LoRA, QLoRA, and proper evaluation methodology
- Design LLM-powered products with proper architecture, routing, and cost controls
- Understand frontier research - MoE, SSMs, reasoning models, and alignment techniques
The Engineering Standard
Every lesson in this curriculum:
- Opens with a real production scenario or research insight you need to understand
- Derives the math from first principles - not handed to you as magic formulas
- Connects theory to working code with production-grade implementations
- Closes with senior-level interview Q&As that test conceptual depth
This is not a tutorial platform. It is an engineering curriculum.
Career Outcomes
Prepared for roles including:
- LLM Engineer
- AI Engineer
- NLP Engineer
- ML Research Engineer
- AI Systems Architect
- Applied AI Scientist
Certification (Coming Soon)
EngineersOfAI - LLM Engineering Certification
Practical. Deep. Research-informed. For engineers who understand LLMs from attention heads to alignment - not just the API.