Beam Deserves an Audition Before It Gets the Keys
A sceptical Renaissance judge auditions three smartphones while astonished scholars plead their case, an allegory for testing AI models. Made by Edward Frank Morris
A new AI model arrives with a procession of impressive numbers, and suddenly everyone behaves as though a particularly charismatic duke has entered the room. I prefer an audition. Use ChatGPT to design a small, repeatable evaluation before deciding whether Reflection's Beam belongs in your workflow.
Reflection's availability announcement describes open weights, its own API and access through multiple partners. The practical opportunity is choice. The practical obligation is discovering which option actually works for you.
Make the audition inconvenient
A demonstration succeeds when everything goes beautifully. Your evaluation should also include the awkward Tuesday when an attachment is missing and someone has named four different files FINAL.
Choose ten examples from the work you genuinely need done. Include incomplete instructions and cases where the correct response is to ask a question. Create expected answers yourself before seeing the model's output. That stops an eloquent mistake from quietly rewriting the marking scheme.
Turn these ten anonymised work examples into an evaluation pack. For each, specify the expected result, essential evidence and unacceptable errors. Keep the same inputs across candidate models. Add a scoring rubric that rewards correctness and appropriate uncertainty. Do not score a model from its marketing claims.
Keep a record that survives enthusiasm
Record the exact model version and settings, including any connected tools.
Measure human correction time alongside task completion time.
Check every factual answer against the underlying source material.
Repeat difficult cases and retain the failures, not just the charming successes.
ChatGPT can help organise the pack and compare recorded results. It cannot substitute a confident prediction for actually running Beam. Nor should its own assessment be the only judge when it is one of the competitors.
Use synthetic or authorised data for the trial. Set the acceptance threshold before testing, and investigate disagreements rather than averaging them into a comforting score.
There is no need to marry a model on the first date. Particularly one that has brought its own league table.
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