What is rule-based reranking?

Rule-based reranking reorders retrieved RAG passages using explicit ranking rules instead of relying solely on similarity scores or a learned model.

How it works

1. Retrieve a shortlist using BM25, dense search, or hybrid retrieval.
2. Inspect trusted metadata such as publication date, document status, source authority, exact identifiers, or product version.
3. Apply defined boosts, penalties, or ordering priorities.
4. Sort the permitted candidates and pass the most relevant evidence with citations to the LLM.

Question → retrieval → business-rule scoring → reordered evidence → answer

Example

A user asks: "What is our current travel reimbursement limit?"

Initial retrieval finds:

• Chunk A: A highly similar but archived 2023 policy.
• Chunk B: The active 2026 policy.
• Chunk C: A general travel FAQ.

A freshness and source-authority rule can prioritize the active policy above the outdated one. Crucially, documents that are unauthorized or explicitly revoked should be excluded with hard filters, not merely demoted.

Common reranking rules

• Freshness: Prefer currently effective documents.
• Authority: Boost approved policy sources over informal notes.
• Exact terms: Favor a matching product code, API endpoint, or error identifier.
• Version: Prefer the version requested by the user.
• Domain priorities: Boost appropriate source categories, subject to evaluation.

A conceptual formula is:

final_score = relevance_score + freshness_boost + authority_boost

This is illustrative. Real systems must calibrate boosts against the retriever's score scale, or use explicit ordering priorities instead.

Rule-based versus model-based reranking

• Rule-based: Explainable, usually inexpensive, and auditable, but limited to encoded signals.
• Cross-encoder or LLM-based: Better able to judge nuanced question–passage relevance, but often more costly.
• Combined: Filter for authorization first, apply relevance reranking, then carefully tuned business priorities.

Risks

Aggressive or stale rules can push irrelevant chunks above answer-bearing passages. Test with representative queries, relevance judgments (for example, nDCG@K), grounded answer quality, and latency.

Interview takeaway

Rule-based reranking adjusts retrieved-document order using transparent signals such as freshness, source authority, exact matches, and version relevance. Use it to express ranking preferences, never as a substitute for access-control or hard-exclusion filters.