Agentic AI Reference Architecture

Knowledge & Data · Model asset

Cross-Encoder Reranking Model

Model assetKnowledge & DataKnowledge & Dataarc:CrossEncoderRerankingModel

A trained cross-encoder model that jointly encodes a query and a candidate document to output a query-document relevance score used for reranking.

Responsibility. Scores query-document relevance for reranking.

Also known as: Cross-encoder

deployed onReranker: deployed onReranker
Direct neighbourhood (hover for relationship types)

Relationships

deployed on structural

Classification

Technologies
Llama 3.2 RerankQA 1B V2
Quality attributes
Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)

Sources

  1. Ch6.5: T. Nguyen, "Production RAG Systems," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 6.5. ISBN: 9798244538229.
  2. Ref7.07: E. Li, V. Bellotti, R. Kraus, and R. Kao, "Build a retrieval-augmented generation (RAG) agent with NVIDIA Nemotron," NVIDIA Technical Blog, Sep. 23, 2025. [Online]. Available: https://developer.nvidia.com/blog/build-a-rag-agent-with-nvidia-nemotron/