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A Field Guide to Rapidly Improving AI Products

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The most successful teams aren’t the ones with the most sophisticated tools or the most advanced models—they’re the ones that master the fundamentals of measurement, iteration, and learning.

The Most Common Mistake: Skipping Error Analysis

The Error Analysis Process

Bottom-Up Versus Top-Down Analysis

The Most Important AI Investment: A Simple Data Viewer

Empower Domain Experts To Write Prompts

Build Bridges, Not Gatekeepers

Tips For Communicating With Domain Experts

Bootstrapping Your AI With Synthetic Data Is Effective (Even With Zero Users)

A Framework for Generating Realistic Test Data

Guidelines for Using Synthetic Data

Maintaining Trust In Evals Is Critical

Understanding Criteria Drift

Creating Trustworthy Evaluation Systems

1. Favor Binary Decisions Over Arbitrary Scales

2. Enhance Binary Judgments With Detailed Critiques

3. Measure Alignment Between Automated Evals and Human Judgment

Scaling Without Losing Trust

Your AI Roadmap Should Count Experiments, Not Features

Experiments Versus Features

The Foundation: Evaluation Infrastructure

Communicating This to Stakeholders

Build a Culture of Experimentation Through Failure Sharing

A Better Way Forward

Resources for Going Deeper

If you’d like to explore these topics further, here are some resources that might help:

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