LLM Mastery course page. This lesson is part 1 of 5 in the beginner track. Use the lab and assessment sections as the completion standard, not optional reading.
Required mastery artifact: by the end of this lesson, update the running enterprise readiness packet for a realistic use case. Treat examples and vendor names as dated illustrations; defend decisions with current model, cost, risk, and evaluation evidence.
LLM Mastery: Enterprise AI Engineering Curriculum
A practical curriculum for building, evaluating, deploying, and governing LLM systems in enterprise environments.
This course is written for engineers, platform teams, product builders, and technical leaders who need to move from LLM concepts to production-grade systems. It still starts from first principles, but the completion standard is enterprise readiness: measurable quality, security controls, governance gates, operational runbooks, and a defensible release decision.
Who This Is For
| Role | What this curriculum prepares you to do |
|---|---|
| AI engineer | Build RAG, fine-tuning, agent, evaluation, and deployment workflows |
| Platform engineer | Operate model-serving, observability, access control, and release pipelines |
| Product engineer | Turn LLM capabilities into usable workflows with quality and cost controls |
| Security/risk partner | Review AI systems for data, access, logging, human oversight, and compliance gaps |
| Technical leader | Decide when to use prompting, RAG, fine-tuning, local models, vendor APIs, or governed deployment |
Prerequisites
- Comfortable reading Python examples.
- Basic API, HTTP, JSON, and command-line familiarity.
- For fine-tuning labs: access to Google Colab, a cloud GPU, or a local CUDA/Apple Silicon environment.
- For enterprise readiness: willingness to document risks, controls, evidence, and release decisions.
Completion Standard
You are done when you can produce the following artifacts for a realistic business use case:
- Use-case brief with user, data, risk, and success criteria.
- Model/system selection decision with cost, latency, privacy, and governance tradeoffs.
- Working prototype using prompting, RAG, fine-tuning, agents, or orchestration as appropriate.
- Evaluation suite with baseline, quality metrics, safety tests, and release thresholds.
- Deployment plan with identity, access control, logging, monitoring, rollback, and incident response.
- Governance packet with risk classification, data review, model inventory entry, human oversight plan, and approval checklist.
Recommended Pacing
| Format | Suggested schedule |
|---|---|
| Self-paced | 4-6 weeks, 2-4 focused sessions per week |
| Engineering cohort | 5 days intensive or 8 half-day sessions |
| Enterprise enablement | 6-8 weeks with weekly labs, review boards, and capstone demos |
How to Use This Curriculum
Read the modules in order unless you already have production LLM experience. Each module has a summary, mental model, mistakes to avoid, and a hands-on exercise. Use the assessment guide to turn exercises into graded enterprise training artifacts.
Evaluation appears late as a full module, but you should introduce its habits early:
- Before building: define the baseline and release threshold.
- During prototyping: collect failure cases.
- Before release: run quality, safety, privacy, and cost gates.
- After release: monitor drift, incidents, and user feedback.
Curriculum Map
Module 01 - Foundations
What is an LLM? How does it work? What should enterprise teams know before choosing one?
| File | Topics |
|---|---|
01-foundations/01-llm-basics.md | What an LLM is, ecosystem, conversations, basic capabilities |
01-foundations/02-how-models-work.md | Neural networks, training, inference, architecture overview |
01-foundations/03-tokens-tokenization.md | Tokens, token budgets, costs, tokenizer behavior |
01-foundations/04-10-remaining-foundations.md | Context windows, embeddings, transformers, attention, parameters, training vs inference, open vs closed models |
Enterprise deliverable: model-selection note explaining cost, privacy, latency, context, and open/closed model tradeoffs.
Module 02 - Datasets & Training
How training data works, how fine-tuning data should be prepared, and why data governance comes before training.
| File | Topics |
|---|---|
02-datasets-training/complete-module-02.md | SFT, instruction tuning, preference data, synthetic data, curation, formatting, fine-tuning basics, continued pretraining, hallucination reduction |
Enterprise deliverable: data card with source, license, sensitivity, PII handling, retention, train/validation/test split, and approval status.
Module 03 - Fine-Tuning
How to customize models responsibly and how to prove the result is better than the baseline.
| File | Topics |
|---|---|
03-fine-tuning/complete-module-03.md | LoRA, QLoRA, DPO, RLHF, quantization, checkpoints, adapters, GGUF |
Enterprise deliverable: fine-tuning experiment report with baseline, dataset version, hyperparameters, eval results, regression risks, and rollback plan.
Module 04 - Inference & Optimization
How models become fast, cheap, and predictable enough for real users.
| File | Topics |
|---|---|
04-inference-optimization/complete-module-04.md | KV cache, Flash Attention, speculative decoding, serving, batching, GPU/VRAM, latency-quality tradeoffs |
Enterprise deliverable: capacity and cost estimate with latency budget, concurrency target, model size, and fallback strategy.
Module 05 - Local AI Ecosystem
The tools used to run, serve, fine-tune, and package local/open models.
| File | Topics |
|---|---|
05-local-ai-ecosystem/complete-module-05.md | llama.cpp, Ollama, vLLM, MLX, Hugging Face, Unsloth, Axolotl, PEFT/TRL |
Enterprise deliverable: toolchain decision record covering supportability, security review, artifact provenance, and operational owner.
Module 06 - RAG & Memory
Retrieval, grounding, citations, memory, and access-controlled knowledge systems.
| File | Topics |
|---|---|
06-rag-memory/complete-module-06.md | RAG, vector databases, chunking, retrieval pipelines, memory systems, semantic search |
Enterprise deliverable: RAG architecture with document ACLs, tenant isolation, source freshness, retrieval metrics, and deletion process.
Module 07 - Agents & Workflows
Tool use, workflows, agents, multi-agent systems, and safe automation boundaries.
| File | Topics |
|---|---|
07-agents-workflows/complete-module-07.md | Prompt engineering, system prompts, tool/function calling, agents, agentic workflows, multi-agent systems, browser agents |
Enterprise deliverable: agent control plan with tool allowlist, scoped credentials, approvals, transaction logs, and human override.
Module 08 - Model Types
How to choose among VLMs, SLMs, MoE models, coding models, and reasoning models.
| File | Topics |
|---|---|
08-model-types/complete-module-08.md | Vision-language models, small language models, dense vs MoE, coding models, reasoning models |
Enterprise deliverable: model fit assessment mapping task complexity to model type, quality target, deployment constraint, and risk level.
Module 09 - Deployment
Production serving, edge/on-device deployment, cloud GPUs, API hardening, and operational ownership.
| File | Topics |
|---|---|
09-deployment/complete-module-09.md | Local inference, on-device AI, API serving, cloud GPUs, edge AI |
Enterprise deliverable: deployment readiness review covering identity, RBAC, secrets, network controls, audit logs, monitoring, SLOs, rollback, and incident response.
Module 10 - Evaluation
How to decide whether an LLM system is good enough to ship and safe enough to operate.
| File | Topics |
|---|---|
10-evaluation/complete-module-10.md | Benchmarks, custom evals, human evals, LLM-as-judge, cost analysis, speed-quality benchmarking |
Enterprise deliverable: release gate report with baseline comparison, quality metrics, safety/privacy tests, cost/latency data, and approval decision.
Module 11 - Real-World Skills
Building usable products and workflows from the technical pieces.
| File | Topics |
|---|---|
11-real-world-skills/complete-module-11.md | Chatbots, copilots, automation, AI SaaS workflows, coding workflows, orchestration, product thinking, final capstone |
Enterprise deliverable: capstone demo and implementation packet for a governed compliance automation product.
Module 12 - Enterprise Governance & Operations
The operating model that makes AI systems approvable, auditable, and maintainable.
| File | Topics |
|---|---|
12-enterprise-governance/complete-module-12.md | AI risk classification, data governance, model/vendor governance, security architecture, eval gates, monitoring, incident response, change management |
Enterprise deliverable: AI system readiness packet suitable for review by engineering, security, privacy, legal, risk, and operations stakeholders.
Reference - Patterns & Anti-Patterns
| File | Topics |
|---|---|
00-design-patterns-antipatterns.md | Production patterns, anti-patterns, decision tables, scenarios |
Use this as a reference during labs and capstone work.
Learning Path Recommendations
New to LLMs: Modules 01, 04, 06, 07, 10, 12, then the Module 11 capstone. Add Modules 02-03 when customization is needed.
Enterprise product builder: Modules 01, 06, 07, 09, 10, 11, 12. Use Module 05 only for local/open-model decisions.
Fine-tuning path: Modules 01, 02, 05, 03, 10, 09, 12. Do not fine-tune without a locked evaluation set and data approval.
Platform path: Modules 04, 05, 09, 10, 12. Focus on serving, identity, auditability, SLOs, cost, rollback, and incident response.
Security/risk reviewer: Modules 01, 06, 07, 09, 10, 12, plus the reference anti-patterns.
Enterprise Training Artifacts
Use these documents to run the course as a formal training program:
- Enterprise Assessment Guide: objectives, rubrics, quizzes, capstone scoring, and facilitator checklist.
- Module 12 - Enterprise Governance & Operations: governance and operations module.
- Design Patterns & Anti-Patterns: field reference for implementation reviews.
Final Note
Understanding beats memorization. For enterprise systems, evidence beats confidence. Build, measure, document, review, and only then ship.