Courses / On-demand courses
Retrieval-Augmented Generation
Chunking, embeddings, reranking, citations, measured.
- recorded hours
- 9
- modules, in order
- 6
- deployed projects
- 4
- graded checkpoints
- 5
Who it is for, and what you receive.
Developers in Python who have made one LLM API call and want a retrieval system they can measure.
Before you start
Python; one LLM API call before.
Skills
- RAG
- embeddings
- vector search
- reranking
- evals
- 1
The recorded sessions
Taught by an instructor who does the work, taken in order at your own pace, yours for twelve months.
- 2
Graded work after every module
Exercises and projects marked by Nova against a rubric you can read, with what you did well and what to fix.
- 3
The projects
Deployed and graded, each one on your record with its repository.
- 4
Your hiring plan
The same six-part plan the bootcamps use, run with the career agent.
- 5
The record and the certificate
Every grade at square1ai.com/u/{handle}; a credential ID that resolves at square1ai.com/verify.
- 6
Nova as your tutor
Help that is about your actual work, because it has read all of it.
The content plan, module by module.
9 recorded hours from an instructor, taken in order at your own pace. Nova grades the work at the end of each module.
- 1
Retrieval fundamentals
- Lexical and semantic search
- Why RAG fails
- Measuring retrieval
Graded
Nothing to submit; watch and take notes
60 min
- 2
Ingestion and chunking
- Parsing
- Chunking strategies
- Metadata
Graded
An ingestion pipeline
90 min
- 3
Embeddings and hybrid search
- Embeddings
- Vector indexes
- Hybrid retrieval
Graded
A hybrid index with recall measured
90 min
- 4
Reranking and generation with citations
- Rerankers
- Grounded generation
- Citations
Graded
A cited answer engine
90 min
- 5
Evaluating RAG
- Retrieval evals
- Faithfulness
- Failure analysis
Graded
A RAG eval suite
90 min
- 6
Project: a RAG application
- Build
- Evaluate
- Deploy
Graded
The application, deployed
120 min
The projects.
Each is deployed and graded by Nova against a rubric you can read before you start.
- 1
An ingestion pipeline
Parse and chunk a real corpus.
You hand in
- Pipeline
- Chunks with metadata
The rubric requires
Reruns idempotently.
- 2
A hybrid index
Hybrid retrieval with recall measured.
You hand in
- Index
- Labelled queries
- Recall report
The rubric requires
Recall at ten reported.
- 3
A cited answer engine
Answers with checkable citations.
You hand in
- Reranker
- Answer engine
- Faithfulness eval
The rubric requires
Faithfulness measured.
- 4
A deployed RAG application
Ship it.
You hand in
- Deployed app
- Eval suite
The rubric requires
Deployed with evals passing.
What you can do at the end.
A RAG system you can measure and four graded projects on your record.
- 1
Explain retrieval fundamentals and where RAG fails.
- 2
Build ingestion and chunking pipelines.
- 3
Build embeddings and hybrid search with measured recall.
- 4
Rerank and generate cited answers with measured faithfulness.
- 5
Evaluate a RAG system end to end and deploy it.
Roles this prepares you for
- AI engineer
- Search engineer
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 the end.
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.
- Module by module
- Each module ends in graded work; the next opens when you are ready, on your own schedule.
- Nova remembers
- Help is about your actual work, because the tutor has every submission and every failed exercise of yours.
- One page an employer can run
- Every grade and project at /verify. An employer opens it and runs the code.
Record, Retrieval-Augmented Generation
Example
- ProjectGraded
An ingestion pipeline
Reruns idempotently.
- ProjectGraded
A hybrid index
Recall at ten reported.
- ProjectGraded
A cited answer engine
Faithfulness measured.
- ProjectGraded
A deployed RAG application
Deployed with evals passing.
- Every moduleGraded
5 graded checkpoints
Each module ends in work Nova grades line by line against a rubric you can read.
- AfterKept
Your hiring plan
Target roles, the gap map, the proof to send, weekly actions and an interview log.
The entries, not the grades: those are yours to earn. The page lives at /verify and an employer needs no account to open it.
6 entries, at your own pace. One email when this course opens.
How we help you find a job.
Proof, not a certificate: the projects you deployed are the thing you show, and the tools below are yours to use.
- 1
Your hiring plan
The same six-part plan the bootcamps use: target roles, the gap map, the proof to send, weekly actions, an interview log, the outcome. You run it with the career agent.
- 2
A record an employer can run
Your graded projects on /verify. An employer opens it and runs the code.
- 3
The career agent
Paste a real job posting at /career and it maps the role to your graded work and what to do next.
- 4
The roles directory
Every role we prepare people for, what it pays and what it asks, at /roles.
- 5
A path to the live cohort
If you want the instructor, the gates and the hiring sprint, the bootcamp on the same subject is one waitlist away.
One email when this course opens.
Recorded by an instructor who does this work, graded by Nova. The waitlist hears the date and the price first, and nothing is charged before it exists.