The Model Was Never the Problem

📰 Dev.to AI

Learn how to evaluate and select models using a live model and scoring system to avoid recall misses masquerading as selection misses

advanced Published 24 Sept 2026

Action Steps

Run the measure/eval_selection.py script to evaluate model selection
Configure the script to use the same tool pools and task pairs as the shortlist
Use the description-set hash to tag result rows and track changes
Compare the results of different models using the scoring system
Apply the evaluation methodology to avoid recall misses masquerading as selection misses

Who Needs to Know This

Data scientists and machine learning engineers can benefit from this approach to model evaluation and selection, as it provides a more accurate assessment of model performance

Key Insight

💡 Using a live model and scoring system can help avoid recall misses masquerading as selection misses

Full Article

The shortlist post ended with a promise: stage two of the eval, selection within the shortlist, measured with a live model, scored so that a recall miss can never masquerade as a selection miss. This post pays that debt. The design came straight from the comment threads: same tool pools, same task pairs, shortlists from the same retriever, and every result row tagged with the description-set hash so the numbers stay attributable across changes. The runner is measure/eval_selection.py in the <

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The Model Was Never the Problem

Full Article

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