What's the most cost-effective way to run relevance scoring at scale using a retrieval evaluation tool?

The most cost-effective approach is usually:

1) Use a lightweight, local scorer first

• Run a smaller open-source model or a simple LLM-based judge locally for most evaluations.
• Cache judgments for repeated query/document pairs.
• Only send ambiguous or high-value cases to a larger model or human review.

2) Batch everything

• Group many relevance judgments into a single request where possible.
• Batch embedding/ranking evaluations offline instead of scoring per query in real time.

3) Pre-filter candidates

• Use cheap retrieval methods first, then score only the top-K results with the more expensive relevance model.
• For example: BM25 or vector search → rerank top 20–100 → final evaluation.

4) Use structured, rubric-based prompts

• A clear binary or small-scale relevance rubric reduces token usage and improves consistency.
• Short prompts and short passages are much cheaper than open-ended evaluation.

5) Sample intelligently

• You rarely need to score every item.
• Evaluate on a representative sample, plus edge cases and difficult queries.
• Stratify by query type, traffic volume, or retrieval source.

6) Reuse and cache aggressively

• If your tool supports it, store judgments by:
  • query ID
  • document ID
  • model version
  • rubric version
• Avoid rescoring unchanged pairs.

7) Separate offline evaluation from production inference

• Don’t use the expensive relevance scorer in the live request path unless necessary.
• Use it offline for benchmarking, regression testing, and periodic audits.

In practice

If you’re using a retrieval evaluation tool, the cheapest scalable setup is often:

• Retrieve with BM25 or vectors
• Rerank with a small cross-encoder or compact LLM
• Judge only the top few candidates
• Cache results
• Use a strong model/human only for uncertain cases

Rule of thumb

For large-scale relevance scoring, the biggest cost savings usually come from:

• fewer scored items
• smaller models
• shorter prompts
• batching
• caching

If you tell me which retrieval evaluation tool you’re using, I can suggest a more specific low-cost setup.