AI evaluation methods

By Clara Gig · Instagram

Source: https://www.instagram.com/reel/DePOkFwss6f/?psln=MXY5bWJucDU2aGJuMw==

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Summary

Clara Gig breaks down four eval methods to stop settling for one-shot AI outputs: self-reflection, self-critique agents, rubric grading, and error analysis logs. Running these checks consistently catches bugs and inconsistencies that a single prompt would miss.

Core ideas

One context card plus 7 ideas

Self-reflection pass: Tell the AI to self-check its own output and fix bugs before showing you version two.
Self-critique agent: A separate agent with no context in the original task hunts for bugs or unsupported claims, feeding into another model for a cleaner check.
Rubric grading system: Build a yes/no checklist with ten criteria, each scored zero or one, and require a minimum passing score like seven out of ten.
Locked criteria: Set up the rubric so the AI cannot edit or influence the checklist itself, keeping the grading honest.
Error analysis spreadsheet: Log every mistake the AI makes so patterns show up over time.
Feedback loop: Feed the most frequent errors from the spreadsheet back into the system to reduce repeat mistakes.
Core mindset shift: Treat one prompt as a draft stage, not a final answer, and build a structured check process around it.

Quotes

“Most people write only one prompt but AI is never going to give you the best output that way.”
Clara Gig

“A rubric grading list is a yes or no checklist zero stands for false and one stands for true.”
Clara Gig

“save this and never stop at one prompt”
Clara Gig

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