Solo developers shipping AI features face silent quality regressions they may never detect without a structured evaluation system. This guide explains how to build a practical eval system without a research team or large budget. It covers three eval types: golden set evals (10–50 hand-curated cases), regression evals (capturing fixed bugs), and production sample evals (real traffic). Scoring combines deterministic code checks with LLM-as-judge for subjective quality. A 14-day rollout plan walks through building a daily automated runner with alerting. Cost-saving tips include using cheaper judge models, prompt caching, and capping golden set size. The guide also covers eval strategies for different AI feature types: generative text, structured extraction, conversational, and agent workflows.
Table of contents
Why Evals Matter More for Solo Developers, Not LessWhat an Eval Actually IsThe Three Types of Evals You Actually NeedHow to Build Your First Golden Set in One AfternoonRunning Evals: Code Checks vs LLM-as-JudgeThe Eval Loop: Making This Part of Your WorkflowEvals for Different Types of AI FeaturesWhat to Do When Your Evals FailThe Cost of Evals, and How to Keep It SaneWhat Not to DoThe Solo Developer Playbook: A 14-Day RolloutThe Honest Bottom LineSort: