Steal This Exam. Here's How to Port It to Your Own Pipeline.
So far this series has been about giving my order-reading LLM an exam . Some of you have been reading it thinking: "Mine isn't orders, it's meeting-minutes summarization." "I'm using it for email triage." Good news — the exam is built to be stolen. Only two things need swapping: the reference data you match against (mine: a product catalog) and the worst accident (mine: wrong goods loaded onto a…
This guide explains how to adapt an exam designed for an order-reading language model (LLM) to fit other applications. The key steps are:
1. Identify critical consequences that cannot be undone if the AI makes a mistake. This serves as your "truck" - something that once shipped, cannot be reversed. Examples include loading wrong goods onto a truck, sending a wrong email reply, or misrecording meeting minutes.
2. Create a grading table where the severity of consequences increases. Fatal consequences that cannot be undone come first, followed by risky, missed, and harmless categories. The first category you define sets the tone for the entire grading scale.
3. Construct exam questions based on potential accidents identified in the first step. Focus on confusable pairs, plausible non-targets, mid-message reversals, and memory errors if the system has a memory component. Aim for at least one question per accident type, with a total of around 10-29 questions to start.
4. Write the answer key, knowing it may be inaccurate. Apply three rules: ambiguous answers should be marked as needing confirmation, the model's answer should be verified against the reference data before penalizing, and check if the answer alone is enough to pinpoint the correct response. Use the flag "needs confirmation" when the catalog information is insufficient for a definitive answer.
5. Grade the AI's responses according to the grading table and record the results. Save the model's answer sheets for future re-grading if needed. Once all tests are complete, create a ledger noting which accidents were successfully identified and which were not. The exam score reflects the author's imagination and cannot account for unimagined scenarios.
In summary, by defining fatal consequences, building a grading framework, planting traps based on potential errors, and carefully crafting the answer key, you can effectively repurpose the exam for various AI applications beyond the original order-reading use case.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
