AI Engineering Mastery
From zero foundation to professional AI work

AI Engineering Mastery

Learn the artificial intelligence engineering skills you actually need for real jobs: Transformers, large language models, retrieval-augmented generation, AI agents, fine-tuning, model literacy, and production infrastructure. No encyclopedia-style detours; each topic is chosen because it shows up in professional AI systems.

Start roadmap Read Attention lesson
tokenvectorscoresoftmaxcontext
Transformer data flow
  1. 1
    Input tokensText is split into model-readable pieces.
  2. 2
    Token embeddings + positional signalEach token becomes a vector, then position is added so order is visible.
  3. 3
    Encoder stackSelf-attention and feed-forward layers build contextual input vectors.
  4. 4
    Decoder stackMasked self-attention looks left; encoder attention looks back at the input.
  5. 5
    Next-token probabilitiesThe model scores possible next tokens and chooses or samples one.
Learning order

Follow the topics in the order you should learn them.

The curriculum starts with Transformer basics, then expands into LLMs, RAG, agents, data pipelines, training, fine-tuning, and production systems. Every module is selected for job-ready understanding, implementation, or debugging work.

01
Foundation

Basics of Transformers

Learn tokens, vectors, embeddings, attention, positional information, and why Transformer architecture changed sequence modeling.

LLM engineeringML researchAI product work
  • Vocabulary
  • Transformer intuition
  • Attention math
  • Paper reading
Open Attention lesson
02
Models

LLM Foundations

Understand next-token prediction, context windows, decoding, prompting, instruction tuning, model limits, and evaluation basics.

AI applicationsModel evaluationPrompt systems
  • Token prediction
  • Prompt behavior
  • Context limits
  • Evaluation basics
Open LLM Foundations lessonExplore model families
03
Applications

RAG Systems

Build systems that retrieve outside knowledge, rank evidence, cite sources, and reduce hallucinations with measurable grounding.

SearchSupport assistantsEnterprise knowledge
  • Embeddings
  • Chunking
  • Retrieval
  • Grounded answers
Open RAG Systems lessonOpen RAG build path
05
Data

Data Pipelines

Prepare, clean, version, and evaluate the data that powers retrieval, training, fine-tuning, and production feedback loops.

RAGFine-tuningEvaluation
  • Data cleaning
  • Versioning
  • Labeling
  • Quality checks
Open Data Pipelines lesson
06
Training

Training Runs

Understand datasets, loss, optimization, checkpoints, validation, regression tests, and how model behavior changes during training.

ML engineeringFine-tuningModel research
  • Loss curves
  • Checkpoints
  • Validation
  • Regression tests
Open Training Runs lesson
07
Adaptation

Fine-Tuning

Learn when to adapt a model with examples, how to format training data, and how to compare the result against prompting or RAG.

Domain assistantsStyle controlTask specialization
  • Training data
  • Eval sets
  • Regression risk
  • Deployment checks
Open Fine-Tuning lessonOpen Fine-Tuning build path
08
Infrastructure

AI Systems

Operate AI features with serving, latency, caching, monitoring, tracing, cost controls, and incident response.

Production appsPlatform teamsReliability work
  • Serving design
  • Caching
  • Monitoring
  • Cost model
Open AI Systems lessonOpen AI Infrastructure build path
Runnable projects

Turn the roadmap into first working systems.

After the foundations, build small projects that exercise the professional skills: evaluation, retrieval, tool use, dataset planning, and release gates.

First build

First LLM Eval Project

Create a tiny evaluation harness that sends the same task examples to two prompts or models, scores the outputs, and prints a pass/fail report.

First build

First RAG App

Build a local question-answering app over a small document folder, retrieve source chunks, and answer only when evidence is available.

First build

First Tool-Using Agent

Build a step-limited agent with one read-only tool, strict argument validation, structured observations, and a trace of every decision.

Builder

First Fine-Tuning Dataset and Eval Plan

Prepare a small supervised fine-tuning dataset plan, split it correctly, define quality checks, and decide whether fine-tuning is justified.

Production gate

First Production AI Observability and Eval Gate

Add tracing, metrics, and a pre-release evaluation gate to one AI workflow so production changes are measurable and reversible.

Open project track