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Prompt Evaluation: Systematically testing and improving your Gen AI prompts at scale.

๐ŸŒˆ Abstract

The article discusses a method for automatically and systematically evaluating and improving prompts at scale for Generative AI workloads. It presents a framework for building a strong testing system using human-curated question-answer pairs as a gold standard, and an LLM-based judge to score the quality of prompt responses. The article also provides sample code and prompts to demonstrate the implementation of this framework.

๐Ÿ™‹ Q&A

[01] Prompt Evaluation and Improvement

1. What are the key challenges with managing prompts in Generative AI workloads?

  • The forest of prompts, prompt templates, and prompt variations can quickly become impossible to navigate and maintain as the system grows.
  • Prompts can become more like a "Frankenstein's monster of fixes and features" over time, making it difficult to understand how the system even works.

2. What is the author's proposed method for addressing these challenges?

  • Start with a set of human-curated question-answer pairs as a gold standard.
  • Create an automation system that runs prompts against the test questions, compares the generated answers to the gold standard, and produces a numerical score on how well the prompt performs.
  • Use an LLM-based "judge" to evaluate the similarity of the generated answers to the correct answers, and provide reasoning for the scores.
  • Summarize the reasoning across all test questions to identify strengths and weaknesses of the prompt.

3. What are the key benefits of this approach?

  • Allows for systematic and scalable evaluation and improvement of prompts.
  • Provides a quantitative measure of prompt quality that can be tracked over time.
  • Helps identify areas where the prompt needs improvement and provides insights on how to improve it.
  • Encourages the development of a robust testing framework, which is a key differentiator between a science project and a production-ready Generative AI system.

[02] Sample Implementation

1. What are the two sample prompts used in the article?

  • Prompt 1: "You are a helpful assistant that loves to give full, complete, accurate answers. Please answer this question:{{QUESTION}}"
  • Prompt 2: "You are a boat fanatic and always talk like a pirate. You do answer questions, but you also always include a fun fact about boats. Please answer this question:{{QUESTION}}"

2. How do the test results for the two prompts differ?

  • Prompt 1 received an average score of 94% and passed 4 out of 5 test questions, indicating it performs well.
  • Prompt 2 received an average score of 56% and passed only 2 out of 5 test questions, indicating it struggles to maintain focus and provide relevant information.

3. What insights does the reasoning summary provide for each prompt?

  • For Prompt 1, the summary suggests the prompt provides high-quality, accurate responses that often go beyond the minimum requirements, with only minor issues around including unnecessary information.
  • For Prompt 2, the summary suggests the prompt has the ability to understand the core concepts and provide creative responses, but struggles to distinguish between relevant and irrelevant information, and to directly address the specific questions asked.
Shared by Daniel Chen ยท
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