What You'll Learn
Take your RAG systems to the next level with advanced embedding techniques, vector databases, and neural re-ranking. This comprehensive course covers the full spectrum of modern retrieval methods that power today's most capable AI systems.
Course Schedule
Week 1 2 sessions
Embedding Models
Deep dive into embedding models including BERT and Sentence-Transformers. Learn how these models encode semantic meaning and how to select the right embedding model for your specific use case.
Vector Search Algorithms
Explore FAISS and Hierarchical Navigable Small Worlds (HNSW) algorithms. Understand the tradeoffs between search speed, memory usage, and retrieval accuracy in these advanced vector search techniques.
Week 2 2 sessions
Vector Database Performance
Compare different vector stores and their performance characteristics. Learn how to optimize your vector database for your specific retrieval needs and scale requirements.
Neural Re-ranking & Custom Embeddings
Master neural re-ranking techniques to dramatically improve retrieval precision. Learn how to train custom embedding models on your domain-specific data for optimal performance.
All times shown in Pacific Time
Where This Class Leads
What you leave able to do
- transform a corpus into an embedded index
- justify a vector index for a latency and corpus budget
- justify an embedding model and its dimensionality
- measure whether a fine tune changed behaviour
- construct a retrieval system
- design a chunking strategy against measured retrieval
- reconcile a failing retrieval by reranking and rewriting
How you show it. A set of questions the first-pass retriever answered wrongly, the reranked or rewritten run, and the before-and-after scores on the same set.
It sits on one path
🤖 Build production AI systems from prompt to deployment. 9 of 12 after RAG & Memory · before Production Agent EngineeringAbout 11 hours of practice sit behind this one. See what your hours buy.
Who Is This Course For
Software engineers, data scientists, and AI practitioners looking to implement advanced retrieval systems for LLMs
Who this is for & what you'll need
Join the Course
Pay what you can - accessible learning for all
We use a suggested price to prevent abuse, but if you need a larger discount, apply for a scholarship. We offer 100% scholarships — no one is turned away for lack of funds.
Suggested price • Pay what you can
- Deep dive into advanced RAG techniques
- Four 90-minute live sessions
- Hands-on experience with embedding models
- Learn vector search algorithms and database optimization
- Master neural re-ranking techniques
- Please self-select into this tier- if you need it, use it
Secure payment via Stripe
Suggested price • Pay what you can
- Deep dive into advanced RAG techniques
- Four 90-minute live sessions
- Hands-on experience with embedding models
- Learn vector search algorithms and database optimization
- Master neural re-ranking techniques
- Help pay for underrepresented folks in tech!
Secure payment via Stripe
Frequently Asked Questions
How are classes delivered?
All classes are live and online via video call. Sessions are recorded so you can review them later if you miss one.
What does "pay what you can" mean?
We show a suggested price, but you can pay any amount. If you need a larger discount, apply for a scholarship — we offer 100% scholarships and no one is turned away for lack of funds.
Do I need prior experience?
🔵 Solo coding. You should be able to write in some language on your own.
What do I get when I enroll?
Live sessions with the instructor, access to course curriculum materials, community access via our Matrix chat server, and recordings of all sessions.
Want to schedule this course for your team?
Contact us: [email protected]