I Built a Hybrid Search Engine From Scratch — Here's What I Learned (LLM Zoomcamp 2026, Module 2)

Short summary

Vector search captures semantic meaning while keyword search matches exact words; neither is perfect alone. Hybrid search using RRF combines both methods to consistently outperform either. The author shares code from LLM Zoomcamp Module 2, demonstrating ONNX embeddings, chunking strategies, and production RAG patterns.

• •Vector search finds meaning, keyword search finds words — hybrid search (RRF) combines both for best results
• •Chunking (2000-char overlapping chunks) is critical for retrieval precision and reduces LLM token costs by 3x
• •ONNX runtime enables practical embeddings anywhere (67MB, no GPU/PyTorch needed) — open-source patterns included

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