Learning Path
LLM Mastery for Enterprise AI Engineering
A free enterprise AI engineering curriculum that turns LLM foundations, RAG, agents, fine-tuning, deployment, evaluation, and governance into one readiness packet.
Course Overview
How to use LLM Mastery as a free enterprise AI engineering course.
What Is an LLM?
The plain-English mental model for large language models and the modern LLM ecosystem.
How AI Models Work
Neural networks, training, softmax, architecture, and why next-token prediction becomes useful behavior.
Tokens and Tokenization
How tokenization affects cost, context windows, latency, multilingual behavior, and practical engineering decisions.
Context, Embeddings, Transformers, and Model Choices
The remaining foundation layer: context windows, embeddings, transformers, attention, parameters, training vs inference, and open vs closed models.
Datasets, Training, and Data Governance
SFT data, instruction tuning, preference data, synthetic data, curation, formatting, and enterprise data cards.
Fine-Tuning with LoRA, QLoRA, DPO, and RLHF
How to customize models responsibly and prove the tuned model is better than the baseline.
Inference and Optimization
KV cache, Flash Attention, speculative decoding, serving, batching, GPU memory, and latency-quality tradeoffs.
Local AI Ecosystem
llama.cpp, Ollama, vLLM, MLX, Hugging Face, Unsloth, Axolotl, PEFT, and TRL.
RAG, Memory, and Access Control
Retrieval-augmented generation, vector databases, chunking, memory systems, semantic search, and enterprise RAG security gates.
Agents, Workflows, and Tool Safety
Prompting, system prompts, tool calling, agents, multi-agent workflows, browser agents, and enterprise tool-use controls.
Model Types and Selection
Vision-language models, small language models, dense vs MoE, coding models, reasoning models, and fit-for-purpose selection.
LLM Engineering Patterns and Anti-Patterns
Production design patterns, anti-patterns, decision tables, and real-world scenarios across the full LLM lifecycle.
Deployment Readiness
Local, on-device, API, cloud GPU, and edge deployment with identity, audit, SLO, fallback, and incident assumptions.
Evaluation and Release Gates
Benchmarks, human evals, LLM-as-judge, cost, speed, safety, privacy, prompt injection, failure severity, and release decisions.
Real-World Skills and Capstone
Build usable AI products and complete the enterprise compliance automation capstone.
Enterprise Governance and Operations
Risk classification, data governance, model/vendor governance, security, human oversight, monitoring, incident response, and change management.
Assessment Guide and Certification Standard
Rubrics, module gates, exemplar artifacts, facilitator checklist, and capstone scoring for running LLM Mastery as a cohort.