Don't guess: How to benchmark your AI prompts

Key Concepts

• Prompt Engineering: The process of designing, developing, and refining prompts for AI models.
• Prompt Churn: The inefficient and aimless modification of prompts without a structured approach.
• Gen AI Evaluation Tool: A Google tool for prototyping and evaluating AI prompts.
• Prompt Ops Framework: A structured methodology for managing and improving AI prompts throughout their lifecycle.
• Craft Stage: The initial phase of prompt engineering, focusing on prototyping and exploration.
• Benchmark Stage: The phase of comparing and evaluating different prompts against each other using defined metrics.
• Integrate Stage: The phase of incorporating prompt testing into automated CI/CD pipelines.
• CI/CD (Continuous Integration/Continuous Deployment): A set of practices that combine software development and IT operations.
• Ground Truth: The correct or expected output for a given input, used for evaluation.
• Temperature (AI parameter): A parameter that controls the randomness of an AI model's output. Lower temperatures lead to more deterministic and repeatable results.
• JSON Schema: A structure that defines the format and constraints of JSON data.

Prompt Engineering with Google Tools: A Prompt Ops Framework

This video introduces a comprehensive framework, "Prompt Ops," for engineering and improving AI prompts, moving beyond guesswork to a disciplined, data-driven approach. The presenter, Martin, outlines a three-stage process: Craft, Benchmark, and Integrate, utilizing powerful tools from Google. The use case demonstrated is filtering spam in a social media app using a single Generative AI prompt, eliminating the need for traditional classifier training.

1. Craft Stage: Prototyping and Initial Evaluation

The initial phase focuses on prototyping and exploring prompt ideas. Google's new Gen AI Evaluation tool is highlighted as a key resource.

• Generate Data Option: This feature allows users to input a prompt template, and the tool automatically generates a dataset for testing. This provides a quick "gut check" on a prompt's viability and identifies areas for improvement.
  • Metrics: Upon evaluation, the tool provides a pass rate and detailed metrics, such as the clarity of generated explanations.
• Upload File Option: For a more realistic assessment, users can upload their own datasets of real-world examples.
  • Flexibility: The tool can even accept pre-recorded responses from any AI model, offering significant flexibility.
  • Side-by-Side View: The evaluation presents a side-by-side comparison of pre-generated results (from the uploaded file) and the output generated by Gemini using the new prompt.
  • Scoring Metrics: Similar to the "Generate Data" option, scoring metrics are provided for evaluation.
• Automatic Prompt Optimization: A notable feature of the Gen AI Evaluation tool is its ability to automatically optimize prompts.

2. Benchmark Stage: Rigorous Prompt Comparison

Once promising prompts are identified, the Benchmark stage involves testing them against each other with the same rigor applied to code testing.

• Google's Python Library: A Python library from Google is introduced for this purpose.
• Colab Notebook Example: A Python Notebook in Google Colab demonstrates the benchmarking process.
  • Key Components: The notebook requires defining:
    • The prompts being compared.
    • The evaluation data (real-world examples).
    • The ground truth (correct answers for each data point).
  • Metrics Definition: Users define the specific metrics to be used for evaluating prompt performance.
  • Evaluation Execution: The library runs the evaluation, which can take a few minutes.
  • Results Analysis: The output provides results for each prompt template against each social media message in the dataset.
  • Visual Comparison: A bar chart visually compares the performance of the two prompts based on the chosen metrics.
  • Metrics Used: The example uses two metrics that measure the similarity between the model's output and the defined ground truth values.
  • Flexibility: The library allows for setting up another AI model to act as a "judge" with custom criteria.
  • Outcome: The core benefit is obtaining hard numbers on prompt performance, moving from guesswork to engineering.
• Alternative Implementation: For users not using Python, a Node.js script is provided in the repository to perform similar evaluations without the specific Google library.

3. Integrate Stage: Automated CI/CD Testing for Prompts

The final stage ensures that the best-performing prompts remain effective over time, especially with potential changes to prompts or underlying AI models. This involves integrating prompt testing into CI/CD pipelines.

• Automated Testing: The goal is to run automated tests against prompts as part of the application's CI/CD pipeline.
• Pipeline Interruption: The script must be able to run independently and stop the pipeline if prompt performance falls below a defined target.
• Realistic Scenario: The script is made more realistic by handling social media posts that can include both text and images, where an image might be spammy even if the text is not.
• Structured JSON Output: The script requests structured JSON output from Gemini, simplifying parsing and avoiding the need to process conversational text.
• Parallel Execution: Multiple Gemini calls are run in parallel to avoid significantly slowing down the CI/CD pipeline.
• Language Agnosticism: The Node.js script demonstrates that this integration can be done in any programming language, even though Google's main evaluation library is in Python.
• Example Execution: The script is run, achieving a 100% pass rate, exceeding the 80% threshold. A success code is returned, indicating pipeline continuation. If it had failed, the pipeline would have stopped.
• Key CI/CD Tricks:
  1. Forced Clean JSON: Using a JSON schema ensures Gemini returns predictable JSON, eliminating parsing complexities.
  2. Low Temperature: Setting the temperature parameter very low (e.g., to 0) ensures more repeatable and deterministic results.
  3. Accuracy Threshold: The script compares the prompt's accuracy against a defined threshold (e.g., 80%) to signal a pass or fail to the CI/CD pipeline.

Conclusion

The Prompt Ops framework, utilizing Google's Gen AI Evaluation tool and Python library, provides a structured and data-driven approach to prompt engineering. By moving through the Craft, Benchmark, and Integrate stages, developers can confidently engineer, improve, and maintain the performance of their AI prompts, ensuring they remain robust and reliable within their applications. This methodology helps to eliminate "prompt churn" and build AI-powered applications with greater predictability and quality.