Advanced RAG Patterns
Implement reranking and hybrid search to significantly improve RAG retrieval quality with measurable metrics.
Lab Overview
🛠Lab from the AI Platform Engineering Bootcamp. Used in Week 3. Bootcamp landing page: https://academy.tekanaid.com/bootcamps/ai-platform-engineering-bootcamp Parent course(s):
- Week 3: RAG Architectures and Vector Databases (slug: rag-architectures-103)
🟡 Beta bootcamp lab. Hands-on instructions, check scripts, and solve scripts are in place. Lab is part of the running Platform Assistant capstone arc that grows across all 8 weeks of the bootcamp.
This hands-on lab teaches you advanced RAG techniques that dramatically improve answer quality.
You'll learn to:
- Implement reranking with cross-encoder models
- Compare retrieval quality metrics with and without reranking
- Build hybrid search combining semantic and keyword matching
- Optimize retrieval pipelines for precision and latency
These techniques transform basic RAG into production-grade systems.
What You'll Learn
Implement reranking with cross-encoder models to improve retrieval precision
Measure and compare retrieval quality using precision and recall metrics
Build hybrid search combining semantic embeddings with BM25 keyword matching
Optimize RAG pipelines for latency and quality tradeoffs
Prerequisites
rag-api-service
Technologies Covered
Part of a Course
This lab is part of the RAG Architectures and Vector Databases course
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