EulerFoldAI Engineering
RAG Pipeline Engineering: pgvector, Hybrid Search, and Re-Ranking
“Learn to build advanced Retrieval-Augmented Generation pipelines. Cover embedding models, hybrid search, and evaluation.”
About this Course
Learn to build advanced Retrieval-Augmented Generation pipelines. Cover embedding models, hybrid search, and evaluation. This AI Engineering curriculum is designed to give you hands-on experience and deep conceptual understanding.
Across 3 intensive modules, you'll tackle real-world challenges and build practical projects that reinforce your learning. By the end of this journey, you'll have the skills and proof of work to demonstrate your expertise.
What you'll learn
Understand embeddings and build a vector store using pgvector.
Combine vector similarity with keyword search for better retrieval.
Evaluate and improve the quality of RAG retrievals while handling complex queries.
W1
Embedding Models and Vector Stores
Understand embeddings and build a vector store using pgvector.
3 videos•69m
2 readings
3 topics
1 homework
W2
Hybrid Search (Dense + Sparse)
Combine vector similarity with keyword search for better retrieval.
3 videos•48m
2 readings
3 topics
1 homework
W3
Advanced Query Routing and Evaluation
Evaluate and improve the quality of RAG retrievals while handling complex queries.
3 videos•54m
1 reading
3 topics
1 homework
01
Learn
Watch curated videos and read study resources
02
Practice
Practice what you learned
03
Build Projects
Build projects using your new gained knowledge
04
Submit & Verify
Submit your project and get verified by our system
References
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