The State of Q1 2026 edition 2026 Galileo Technologies, Inc. Research Report Eval Engineering

2026

Open publication workspace · Sign in to read the full PDF.

AI-generated summary

This report reveals how elite AI teams achieve superior outcomes by focusing on specific, learnable practices and overcoming common paradoxes in evaluation engineering.

* Key findings on what separates elite AI teams from average ones, including data on outcomes, coverage, and reliability.
* An in-depth look at five elite behaviors, such as front-loading testing, systematizing learning from failures, and investing in purpose-built tools.
* Analysis of four paradoxes encountered in AI evaluation, including the perception of investment as failure and the challenges of LLM-as-judge.

Tags: AI, Evaluation, Engineering, Reliability, Best Practices

Check the original publication for accuracy and context.