Lesson 1113 of 1596
Prompt Evaluation and Testing: From Vibes to Rigorous Evals, Part 2
Get a self-estimated confidence number you can route on, without pretending it is perfectly calibrated.
Creators · Prompting · ~24 min read
The premise
A rough confidence number, even imperfect, beats no signal at all when routing humans into the loop.
What AI does well here
- Ask for a 0-100 score with a one-line rationale
- Route low-confidence answers to humans
What AI cannot do
- Trust the absolute number
- Replace measured calibration on real data
Understanding "Asking Claude and GPT for calibrated confidence scores" in practice: Prompts are the primary interface to language model capability. Precision in prompt structure directly maps to output quality. Get a self-estimated confidence number you can route on, without pretending it is perfectly calibrated — and knowing how to apply this gives you a concrete advantage.
- Apply calibration in your prompting workflow to get better results
- Apply uncertainty in your prompting workflow to get better results
- Apply self-evaluation in your prompting workflow to get better results
- 1Rewrite one of your best prompts using role + context + task + format
- 2Ask an AI to critique your prompt and suggest improvements
- 3Compare outputs from two models using the same prompt
Key terms in this lesson
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