LLM Inside-Out Mastery Program

From strong software engineer to seasoned LLM engineer. Build production LLM systems, gateways, RAG pipelines, agents, and a startup on top of this infrastructure.

What This Is

This is a production-grade training curriculum for software engineers who want to master LLM engineering from first principles through startup-grade deployment.

This is not a shallow intro. It is a complete documentation repository that teaches:

How LLMs work (transformers, tokenization, generation, KV cache)
How to read model cards, system cards, pricing pages, and benchmarks
How to evaluate and choose models for real use cases
How to run models locally (llama.cpp, Ollama, MLX, LM Studio)
How to serve models in production (vLLM, TGI, SGLang, TensorRT-LLM)
How to build LLM gateways (OpenRouter-style, LiteLLM-style)
How to build RAG pipelines, agent systems, and AI coding tools
How to evaluate quality, cost, latency, and safety
How to build a startup-grade product on top of LLM infrastructure

Start Here

→ START_HERE.md — Read this first. → ROADMAP_90_DAYS.md — 90-day learning plan. → ROADMAP_12_MONTHS.md — 12-month mastery path.

Repository Structure

llm-mastery/
  README.md                          ← This file
  START_HERE.md                      ← Entry point
  ROADMAP_90_DAYS.md
  ROADMAP_12_MONTHS.md

  00-orientation/                    ← What LLM engineers actually do
  01-llm-vocabulary/                 ← Complete terminology guide
  02-transformer-foundations/        ← How LLMs work internally
  03-model-cards-and-system-cards/   ← How to read model documentation
  04-model-catalogs-and-trend-reading/ ← models.dev and catalog navigation
  05-model-selection/                ← How to choose models
  06-local-inference/                ← Run models on your hardware
  07-production-serving/             ← vLLM, TGI, serving at scale
  08-llm-gateways/                   ← Build OpenRouter-style gateways
  09-rag/                            ← Retrieval-Augmented Generation
  10-agents-and-tools/               ← Agent loops, tool calling
  11-ai-coding-platforms/            ← Cursor-style IDE architecture
  12-evaluation/                     ← Eval, benchmarks, LLM-as-judge
  13-fine-tuning-and-adaptation/     ← LoRA, QLoRA, SFT, DPO
  14-security-privacy-and-governance/ ← Prompt injection, PII, audit
  15-startup-playbook/               ← Build an AI startup

  labs/                              ← Hands-on projects
  diagrams/                          ← ASCII and Mermaid diagrams
  cheatsheets/                       ← Quick reference cards
  templates/                         ← ADRs, memos, model-selection docs
  exercises/                         ← Practice problems
  interview-prep/                    ← Role-specific interview guides
  references/                        ← Annotated bibliography

Who This Is For

You are a software engineer who:

Understands backend architecture, APIs, databases, and cloud infrastructure
May not yet be fluent in LLM/AI terminology
Wants to join or build a high-caliber AI engineering team
Wants to build LLM products, RAG systems, agents, gateways, or self-hosted model infrastructure
Wants practical production labs, not vague overviews

What You Will Be Able to Do

After completing this curriculum, you will be able to:

Skill	Example
Interpret LLM terminology	Read a model card and extract everything relevant
Read model catalogs	Use models.dev to choose the right model for a task
Run local models	Run Llama or Qwen locally with llama.cpp or Ollama
Serve models in production	Deploy vLLM with continuous batching and streaming
Build an LLM gateway	Route, fallback, meter, and log multi-provider usage
Build a RAG system	Ingest docs, chunk, embed, retrieve, and generate with citations
Build agents	Tool-calling loops with approval, rollback, and observability
Evaluate models	Run eval harness, score quality/cost/latency/safety
Estimate cost	Calculate unit economics for any LLM product
Build a startup	Design, cost, and launch an AI-native product

Required Reading Before Starting

Primary External References

Resource	URL	Use
models.dev	https://models.dev/	Model catalog explorer
OpenRouter docs	https://openrouter.ai/docs/	Gateway/routing reference
LiteLLM docs	https://docs.litellm.ai/	Proxy/adapter reference
vLLM docs	https://docs.vllm.ai/	Production serving
llama.cpp	https://github.com/ggml-org/llama.cpp	Local inference
Ollama docs	https://github.com/ollama/ollama/tree/main/docs	Local runner
Unsloth	https://unsloth.ai/docs/models/mtp	GGUF/MTP local models
Hugging Face	https://huggingface.co/docs/hub/model-cards	Model card format
vLLM architecture	https://blog.vllm.ai/	Serving deep dives
OpenAI API	https://platform.openai.com/docs	API baseline

License

This curriculum is intended for personal study and professional development.