Neural Reranking: Precision That Matters
Use state-of-the-art cross-encoder models to reorder search results by true relevance.
First Results Are Not Always Best
How Neural Reranking Works
Initial Retrieval
Hybrid search returns top candidate documents quickly.
Cross-Encoder Analysis
A neural model examines query-document pairs together for deep relevance scoring.
Precision Reordering
Results are reordered by true relevance, boosting the best answers to the top.
Why Neural Reranking Matters
+25% Precision
Top results are significantly more relevant after reranking.
Better Answers
LLM gets the most helpful context, producing better responses.
Handles Nuance
Understands subtle differences between similar documents.
Configurable Depth
Choose how many results to rerank based on your latency needs.
Reranking Capabilities
| Feature | RAG Engine | Chatbase | CustomGPT | Dify |
|---|---|---|---|---|
| Cross-Encoder Reranking | Partial | |||
| Configurable Depth | ||||
| Multiple Models | ||||
| Latency Control | Partial |
Based on publicly available feature lists as of 2024
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