RAG Optimization · Interactive Lab Series

Why does your RAG
give bad answers?

Basic RAG works. Optimized RAG works well. This lab series teaches you the 4 levers that separate them — hands-on, visual, and interactive.

Chunk

→

Embed

→

Index

→

Retrieve

→

Re-rank

Basic RAG & Why It Fails

Start with poor defaults. See mediocre retrieval with large chunks, no overlap, and only 2 retrieved chunks.

Chunking Strategies

Compare Fixed-size (3 sizes), Sentence-window, and Hierarchical chunking. See how chunks look and how retrieval changes.

Embedding Model Impact

Ada 002 vs v3-small vs v3-large. Same query, three models — see different chunks retrieved and different scores.

Vector-only vs BM25-only vs Combined. See what each misses and why combining both gives better recall.

Hybrid Ratio Tuning

Move the α slider from 0.0 (pure BM25) to 1.0 (pure vector). Watch results change live. Find the sweet spot.

Before and after Cohere cross-encoder re-ranking. Watch ranks shift with animated ↑ ↓ indicators.

Built on NovaCorp HR Policy — a fictional document the LLM has never seen. All labs use the same document for continuity. Start at Lab 1 and work forward.