AutoEvals

Five hard earned lessons about Evals — Ankur Goyal, Braintrust

Aug 23, 2025 · 19:46

Ankur Goyal of Braintrust argues that successful AI applications depend on deliberately engineered evaluations (evals) to reflect real user feedback and drive product improvements. He details three signs of effective evals: launching updates within 24 hours (citing Notion), converting complaints into evals, and using evals offensively to assess use cases before shipping. Evals require custom scorers as specs—not off-the-shelf—and context engineering (optimizing tool definitions and outputs) is the new frontier; shifting outputs from JSON to YAML improves token efficiency. New models can upend everything, as shown by a benchmark that jumped from 10% to viable with Claude 4 Sonnet, so a model-agnostic architecture with proxies is key. Optimize the whole system (data, task, scoring)—Braintrust's Loop auto-optimizes prompts, data, and scorers. In Q&A, he advises human judgment when adding user feedback to evals to avoid overfitting.

Evals 101 — Doug Guthrie, Braintrust

Jun 27, 2025 · 48:31

Braintrust solutions engineer Doug Guthrie presents the full AI evaluation lifecycle, covering offline and online strategies for building robust AI products. He explains that evals require three ingredients—tasks (prompts or agentic workflows), datasets (real-world examples), and scores (LLM-as-a-judge or code-based)—and demonstrates how to run them in Braintrust's playground and via SDK. The talk emphasizes creating a feedback loop: production logs feed into datasets, enabling human review and user feedback to iteratively improve offline evals. Guthrie showcases a changelog app, the new AI-driven "loop" feature for prompt optimization, and answers audience questions on A/B testing models, handling multiple human scorers, pre-launch validation, and exporting data for custom dashboards.