Self-paced learning path · 6 lessons + capstone
Applied AI Engineer Learning Path
Learn model APIs, embeddings, retrieval, RAG, evaluation and reliability through six focused lessons and a reproducible document-assistant capstone.
Metarix Editorial · Material reviewed Nov 8, 2026
Start with lesson 1 →Who this path is for
People who can write basic Python functions, read files and use a terminal, and want to build applications around existing AI models. This is an introductory applied-engineering path, not a full machine-learning training curriculum.
Before you start
- Write and call a basic Python function; understand lists and dictionaries.
- Read and edit a text file and run commands in a terminal.
- Use Python 3.10+ for the capstone; no provider account is needed for default offline mode.
What you will practice
- Define application contracts and validate structured model responses.
- Explain embeddings, retrieval and vector stores, and evaluate a lexical baseline.
- Design a source-grounded retrieval-and-generation workflow with explicit evidence boundaries.
- Build versioned evaluation cases for answer quality, unsupported questions and access restrictions.
- Run and extend a reproducible local document assistant, and document what remains before production deployment.
Build a small, source-grounded application around an existing language model rather than starting by training a model from scratch. This path connects the pieces that turn an AI demonstration into an inspectable engineering project: input contracts, model APIs, retrieval, evaluation, access boundaries, and release decisions.
Start here if you can write a Python function, read a file, and run commands in a terminal. You do not need prior model training experience or calculus for these lessons. If Python itself is new to you, begin with the official Python tutorial and return when those tasks feel comfortable.
What you will build
The running project is a document assistant for a fictional support handbook. It retrieves permitted source material, returns a cited result or an abstention, and exposes failure cases through repeatable checks. You will understand where embeddings, vector databases, and RAG fit, and how to decide whether a more complex component improves the application.
The downloadable capstone starts with lexical retrieval and extractive offline output. It runs without a provider account or paid call. An explicitly selected optional adapter adds live generation; that integration is documented and mock-tested, rather than presented as a measured live-model result. Default mode does not compute embeddings or perform generative RAG.
How to follow the path
Work through the six lessons in order, completing each exercise and keeping a record of inputs, expected behavior, and observed failures. Then run the capstone from a fresh directory, add evaluation cases, and make one improvement whose effect you can assess.
You may read the explanations without running code, but completing the practical path requires executing the exercises. Expect to revisit retrieval or source preparation when an evaluation reveals a gap. A single model change is not always the right fix.
What completion means
You should finish with a reproducible local project, a versioned evaluation set, and a written account of what the system can and cannot do. That is a foundation for further applied AI work, not a complete professional qualification or a guarantee of employment.
This path focuses on integrating existing models and operating the surrounding application. Training predictive models, data splitting, feature engineering, and the deeper mathematics of machine learning belong to a different learning track. A hosted production service also needs controls that this teaching CLI intentionally does not implement.
The material was reviewed on November 8, 2026. Provider integrations and operating requirements can change; check the linked primary documentation before a live deployment.
Your learning sequence
Lesson 1 · 5 min read
Applied AI Foundations: Python, APIs and JSON
Define application inputs and outputs, validate a source collection, and separate errors from unsupported questions.
Read the lesson →Lesson 2 · 5 min read
LLM APIs: Prompts, Structured Outputs and Validation
Design a model-response contract and reject malformed, incomplete, uncited or unsupported outputs.
Read the lesson →Lesson 3 · 5 min read
Embeddings and Retrieval: Where Vector Databases Fit
Explain embedding similarity, build a lexical baseline, and define a fair retrieval comparison with access filtering.
Read the lesson →Lesson 4 · 5 min read
RAG Explained: Build Answers from Retrieved Sources
Design a retrieve-then-generate flow with source metadata, context boundaries, citation checks and abstention.
Read the lesson →Lesson 5 · 5 min read
Evaluate an AI App: Retrieval, Answers and Failures
Create versioned acceptance cases, diagnose retrieval versus answer failures, and interpret results without inflated accuracy claims.
Read the lesson →Lesson 6 · 5 min read
Make an AI App Reliable: Access, Cost and Deployment
Specify trusted access, bounded requests, failure handling, observability and staging release gates.
Read the lesson →Final capstone project · 6 min read
Applied AI Capstone: Build a Source-Grounded Assistant
Run the reproducible project, add failure cases, make one measured improvement, and document the remaining production requirements.
Build the capstone →