[Prompt] ND-ReRanker v 0.1

Prompt Card

goal: Replaced the encoder-based reranker with a decoder that generates the reranking output.

target model: microsoft/phi-4 [Q4-K_M: 9.05GB]

refine model: GPT‑4o (ChatGPT)

available input: 8k tokens

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description

This prompt defines a decoder-based ranking evaluator designed to reorder candidate documents from a RAG retrieval pipeline based on semantic relevance. It mimics cross-encoder scoring behavior while ensuring strict output format compliance.

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core tasks

• Analyze query intent (definition, method, comparison, factual detail).
• Rank docs by semantic coverage, not keyword overlap.
• Enforce redundancy filtering and penalize near-duplicates.
• Produce cross-encoder-like scores (0.0–1.0) with realistic gaps.

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scoring policy

• 0.95–1.00 → Perfect intent match.
• 0.75–0.89 → Strong relevance.
• 0.40–0.70 → Partial relevance.
• <0.30 → Low/no relevance.
• Maintain steep score gaps; avoid uniform decrements.

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output format

json
{
  "rank": [doc_id, ...],
  "score": [float, ...],
  "ignore": [doc_id, ...]
}