Engineers who need retrieval to work in production, not just in a demo. Comfort with an API and basic data structures is enough; the embeddings and retrieval theory are taught.

Will it cover why RAG demos fall apart in production?

That is the spine of the course: chunking strategy, hybrid search, and measuring groundedness and recall, so you can diagnose bad retrieval instead of guessing.

Does it lock me into a specific vector database?

No. The principles apply across pgvector, Pinecone, and keyword-plus-vector hybrids, and the course is explicit about the trade-offs.

lrnit

For engineers

Learn RAG systems in a course built only for you. And actually retain it.

Learn why most RAG demos fall apart in production, and how to build retrieval pipelines with real chunking strategy, hybrid search, and measurable answer quality.

Free to start · no account needed

Built around your goal, not a catalog

What your course could open with

lrnit interviews you first (what you know, why you’re learning, how you think), then generates every lesson for that. Sample lessons from a course like yours:

01Embeddings as geometry: why similar means close
02Chunking is product design: fixed windows vs. semantic splits
03Hybrid retrieval: when keyword search beats your vector database
04Measuring RAG: groundedness, recall, and the eval set you must build first
…and the rest is generated for you: your gaps, your pace, your job.

Measured, not watched

By the end, you’ll be able to

Diagnose why a RAG system retrieves the wrong context
Choose chunking and indexing strategies from evidence, not vibes
Ship retrieval quality metrics alongside the feature

What a capability check looks likebeforenow

Choose a chunking strategy+62

Set up hybrid retrieval+64

Measure groundedness+67

Every lesson ends in assessment. Mastery is tracked per concept with spaced review, and the course adapts to what you actually retain. Finishing means a timed final exam and a verifiable certificate, not a completion badge.

“It felt like the course already knew what I did for a living.”

Early lrnit learner

Questions people ask

Who is this for?: Engineers who need retrieval to work in production, not just in a demo. Comfort with an API and basic data structures is enough; the embeddings and retrieval theory are taught.
Will it cover why RAG demos fall apart in production?: That is the spine of the course: chunking strategy, hybrid search, and measuring groundedness and recall, so you can diagnose bad retrieval instead of guessing.
Does it lock me into a specific vector database?: No. The principles apply across pgvector, Pinecone, and keyword-plus-vector hybrids, and the course is explicit about the trade-offs.

Your version of this course doesn’t exist yet.

Free to start · no account needed

More outcomes

Other courses people build here

Browse all outcomes