Adding Epistemic Hooks to Your Workflow: From pip install to Measured AI in 5 Minutes

This guide outlines a comprehensive approach to measuring and improving the performance of AI tools, specifically focusing on how they handle tasks within software development contexts. The process involves several key steps:

1. Initialization: Setting up the environment by installing necessary packages like empirica and configuring it with your project details.
2. Epistemic Vectors: Using a structured approach to quantify the AI's knowledge, uncertainty, clarity, and other relevant factors before and after task execution.
3. Grounded Verification: Ensuring that the AI’s self-assessment is accurate by comparing its claims against objective evidence such as test results, code changes, and quality metrics.
4. Calibration Scores: Evaluating how well the AI predicts its own performance over time to identify areas for improvement.

Key Concepts

• Epistemic Infrastructure: The framework used to measure and track an AI's knowledge and learning process.
• Artifact History: A searchable record of findings, unknowns, dead ends, decisions made during task execution.
• Learning Deltas: Measurable improvements or stagnations in the AI’s performance over multiple sessions.
• Grounded Evidence: Objective data that validates the AI’s self-assessment without relying on subjective reports.

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