How long is the Gen AI Bootcamp?

12 weeks at about 15 hours a week. The weeks run as six two-week blocks; each block ends in a project you deploy and a gate you must pass before the next block opens.

Is the Gen AI Bootcamp live or self-paced?

Live. It is taught on Zoom by one instructor, with a 30-minute one-to-one every week. Nova, Square 1's AI tutor, grades every exercise and project between sessions against a rubric you can read.

When are the live sessions for the Gen AI Bootcamp?

Live sessions run on weekday evenings from 5:30 PM Sri Lanka and India time, with full-day sessions at weekends. That is 5:00 PM in Pakistan, 5:45 PM in Nepal and 6:00 PM in Bangladesh, so the sessions fit around a working day.

How much does the Gen AI Bootcamp cost?

A$200 for the founding cohort, paid once; the standard price is A$400. It is the same price in every country. Founding students keep their founding price on any second programme.

What will I build in the Gen AI Bootcamp?

6 deployed projects: Structured assistant, RAG over real documents, Tool-using agent, Eval harness, Capstone and Employer brief. Each is graded against a published rubric and kept on a record an employer can open at /verify.

What skills does the Gen AI Bootcamp teach?

Python, LLM APIs, prompt engineering, RAG, agents, MCP, evals and observability. It prepares you for roles such as AI engineer, LLM engineer, Applied AI engineer and ML engineer (LLM).

Does the Gen AI Bootcamp guarantee a job?

No. No placement rate is published because the first cohorts have not graduated. What you leave with is six deployed projects, a recorded defence of your code and a hiring plan worked through with your instructor, including a two-week hiring sprint at the end.

Live bootcampTaught live on Zoom, one instructor

Gen AI Bootcamp

Build and ship LLM products: prompting, retrieval, agents, evals, production.

Gen AI Bootcamp is a 12-week live online bootcamp, taught on Zoom by one instructor at about 15 hours a week, with 6 deployed projects and a one-to-one every week. The founding price is A$200, down from A$400. Live sessions run on weekday evenings from 5:30 PM Sri Lanka and India time, with full-day sessions at weekends.

Taught with

PythonClaudeChatGPTLangChainVercel

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By week 12, you will have shipped

Week 1Prompt LabA prompt notebook that compares three prompts on the same inputs and records the outputs side by side
Weeks 1–2LLM foundationsA deployed assistant with structured output and a test suite
Week 3Embedding ExplorerA small script that embeds a document set and returns the closest passages for a question
Weeks 3–4RetrievalA RAG application over a real document set, deployed with citations
Weeks 5–6AgentsAn agent that completes a multi-step task with tools, deployed with traces
Week 7Golden SetA labelled set of questions and answers for your RAG app, with a scoring script
Weeks 7–8Evals and safetyAn eval harness that runs in CI for the projects so far
Week 9Cost TrackerA logging layer that records tokens, latency and cost for every LLM call to a dashboard
Weeks 9–10ProductionThe capstone: a production LLM product with monitoring and a cost model
Weeks 11–12Employer brief and hiring sprintThe employer brief shipped, and demo day

weeks, live on Zoom: 12
hours a week: 15
projects to build: 10
minutes 1-1, every week: 30

Who it is for, and what you receive.

Developers who want to build with language models properly, from the first API call to a monitored product. No machine-learning background needed.

Before you start

Python and Git; no machine-learning background needed.

Skills

PythonLLM APIsprompt engineeringRAGagentsMCPevalsobservability

A week, about 15 hours

Live on Zoom: 4h
Recorded lessons and exercises: 6h
The project: 5h

1
Six deployed projects
Each graded by Nova against a published rubric and signed off by the instructor at the gate.
2
The recorded lessons
The track's lessons and exercises, graded line by line, open for twelve months after the cohort ends.
3
Live on Zoom every week
A 90-minute live code review, a 60-minute squad lab, 60 minutes of office hours, and a 30-minute 1-1 with your instructor.
4
Your hiring plan
Opened in week 1 and reviewed in every 1-1: target roles, the gap map, the proof list, weekly actions, an interview log, the outcome.
5
The record
Every grade and every project with its repository at square1ai.com/u/{handle}, and the recording of your viva, verifiable by any employer.
6
The certificate
A credential ID that resolves at square1ai.com/verify to the real completion.
7
The hiring sprint
Weeks 11 and 12: CV and portfolio from graded work, applications, mock interviews scored against the role, demo day.
8
Nova for twelve weeks
A tutor with every submission of yours in its memory, at 2am as well as in class.

Twelve weeks in six parts.

Each part teaches for a week, then you build and deploy a project, then a gate checks it before the next part opens. The project and its gate are the part's entry on your record.

Live time each week on Zoom: a 90-minute class where the instructor reviews real submissions, a 60-minute squad lab, 60 minutes of office hours, and your own 30-minute 1-1. Nothing is lectured live; the recorded lessons do that.

Key skillsHow language models work, in practicePrompting with structure and constraintsThe Claude and OpenAI APIs: tools, streaming, caching
Week 1
LLM foundations
- Tokens, context and sampling
- What models do well and badly
- Prompting with structure, constraints and examples
- The Claude and OpenAI APIs: structured output, caching, batch
You build: A structured-output assistant
Week 2
Project 1: the assistant
- Schema validation
- Test suites on held-out cases
You build: Deployed assistant with a checked schema and tests
Project 1 · the gate at week 2
Structured assistant
A deployed assistant with structured output and a test suite
You hand in
- Deployed API
- JSON schema
- Prompt library
- Test suite on a held-out set
The gate
Valid, checked output on the held-out set at the stated rate.

What you can do by week 12.

Six deployed LLM applications that cover the whole surface an AI engineering interview asks about, an eval harness you will reuse on every future project, and a recorded viva on a production system.

1
Explain how language models produce output well enough to predict where they fail.
2
Build an assistant on the Claude and OpenAI APIs with structured output, tool use, streaming and caching, and test it.
3
Build a retrieval-augmented application over a real document set and measure retrieval quality and answer faithfulness.
4
Build an agent that completes multi-step tasks with tools, with traces for every call.
5
Write an eval harness with LLM judges and regression suites that runs in CI and catches a bad prompt change.
6
Ship an LLM product with observability, a cost model and an incident process.

Roles this prepares you for

AI engineerLLM engineerApplied AI engineerML engineer (LLM)

No placement rate is shown, because there are no graduates to count yet. The roles above are what the projects are built for.

Your record at week 12.

Every exercise and project is graded by Nova against a rubric you can read, and every grade is kept on one page an employer can open and run. This is what the programme writes to it.

Graded, line by line: Nova reads every submission against the brief and the rubric and returns a score, what you did well and what to fix.
Six gates: A block does not open until the previous project passes its gate. You always know where you are and what is next.
A weekly 1-1: Thirty minutes with your instructor, who has already read your code before the call.
One page an employer can run: Every grade, project and the viva recording at /verify. An employer opens it, runs the code and watches you defend it.

Record, Gen AI BootcampExample

Week 2
Structured assistant
The assistant returns valid, checked output on a held-out set
Graded, gate signed
Week 4
RAG over real documents
Retrieval precision and answer faithfulness measured against a labelled set
Graded, gate signed
Week 6
Tool-using agent
The agent's traces show every tool call and its outcome
Graded, gate signed
Week 8
Eval harness
A regression fails the build; a jailbreak attempt is caught
Graded, gate signed

The entries, not the grades: those are yours to earn. The page lives at /verify and an employer needs no account to open it.

8 entries, twelve weeks. Reserving a spot holds the founding price.

Reserve a seat

How we help you find a job.

The last block is not curriculum. It is the hiring sprint, and the proof you built in the ten weeks before it.

The course booklet

The whole plan on a few pages: the parts, the projects, the gates and the price. We email it to you.

Get the booklet

1
Your hiring plan, from week 1
Six parts you and your instructor keep: target roles, the gap map from real postings, the proof to send, weekly actions, an interview log, the outcome. Read before every 1-1.
2
The hiring sprint
Weeks 11 and 12: CV, portfolio, applications, mock interviews, and demo day in front of hiring partners.
3
A record an employer can run
Your deployed projects and the recorded viva on /verify. An employer opens it, runs the code and watches you defend it.
4
The career agent
Paste a real job posting at /career and it maps the role to your graded work and what to do next.
5
The roles directory
Every role we prepare people for, what it pays and what it asks, at /roles.

Questions people ask.

About Gen AI Bootcamp, answered from the plan on this page.

One cohort, one instructor. Hold your spot.

A cohort of 25, one instructor, 12 weeks, six things you ship. Spot holders hear the start date first, and nothing is charged before you confirm.

Six deployed LLM applications, from an assistant to a monitored product
An eval harness you will reuse on every future project
A recorded viva on a production system
Your record on /verify

A$200~~A$400~~