baseline-before-modellisted

# Run the boring thing first On an address-resolution task, a deterministic directory lookup with an alias table and edit distance scored **96.7%**. The language models given the same items and no reference data scored 65.0% and 53.3%. The lookup ran in 2.8ms at zero cost against roughly 2,000ms and real spend. That result is not an argument about model quality. It is an argument about architecture. A prompt-only model is the wrong tool for a lookup, and no leaderboard will ever tell you that, because "should this be a model at all" is not a property of any model on it. ## The refusal **Do not recommend a model for a task with no measured non-model baseline.** If nobody has built the boring version, the honest output is "unknown, and here is the baseline to build first". ## Candidate baselines, in order of how often they win 1. **Lookup against a table or directory** you already own. Extraction and normalisation tasks are frequently this in disguise. 2. **Rules or a decision table**, when the logic is genuinely small and stable. 3. **Classical retrieval** (BM25) before any embedding pipeline. 4. **A small local model** before a frontier API model. 5. **The existing process**, measured. Often nobody has ever scored the humans. ## Procedure 1. Write the baseline behind the **same interface** the model would use, so the whole evaluation stack runs on it unchanged. 2. Score it on the same items, with the same grader. 3. **State the caveat that travels with the numbe