Overview
Purpose of this cookbook
This cookbook provides a practical guide on how to use the OpenAI Platform to easily build resilience into your prompts.
A resilient prompt is one that provides high-quality responses across the full breadth of possible inputs.
Prompt resilience is an essential piece of deploying AI applications in production. Without this property, your prompts can produce unexpected results on edge cases, provide subpar responses in normal cases, and undermine the effectiveness of your AI application.
To build resilience into your prompts, we recommend the evaluation flywheel process — a methodology that enables builders to continuously refine their AI applications over time in a measurable way.
Target audience
This cookbook is designed for subject-matter experts, solutions architects, data scientists, and AI engineers who are looking to improve the general consistency and quality of their prompts, or address specific edge cases in their AI applications.
The evaluation flywheel
AI applications often feel brittle. A prompt that works well one day can produce unexpected and low-quality results the next. This happens because prompts can be sensitive to small changes in user input or context. To build reliable AI products, we need a systematic way to make prompts more resilient.
The solution is a continuous, iterative process called the evaluation flywheel. Instead of guessing what might improve a prompt ("prompt-and-pray"), this lifecycle provides a structured engineering discipline to diagnose, measure, and solve problems.
The flywheel consists of three phases:
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Analyze: Understand how and why your system is failing through qualitative review. Manually examine and annotate examples where the model behaves incorrectly to identify recurring failure modes.
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Measure: Quantify the identified failure modes and set a baseline. You can’t improve what you can’t measure. Create a test dataset and bu