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LLMs, GenAI, RAG, agents, evaluation · insight-0564

Split the eval set into a development set for iteration and a locked test set for decisions

Evidence type
practitioner heuristic
Difficulty
medium
Roles
Data scientist, Applied AI
Industries
cross-industry

Summary: situation, action, reason, boundary

  1. Situation

    A team iterates on prompts for weeks against one 300-item eval set and reports the final score as expected production quality.

  2. Action

    Keep a development split for daily iteration and a locked test split scored only at decision points; log how many times the test split was used, and refresh it when that count grows.

  3. Reason

    Selecting among many variants on one set overfits that set; this is the multiple-comparisons and selection-bias problem from classical validation.

  4. Boundary

    Prompt engineering, fine-tune selection, retrieval tuning.

Symptom or risk

Dozens of prompt variants were chosen against the same items, so the final score is optimistic, as with repeated test-set peeking.

Exceptions

Very small eval sets may need cross-validation-style rotation instead of a fixed split.

Trade-offs

Fewer items for iteration and some slowdown at decision points.

Pitfalls

Peeking at test failures and fixing those specific items.

Lessons and concepts