Topic
All digests tagged LLM Evaluation (Evals)
Suggested route
Move from earlier context through the available material to the newest signals. This route is a reading aid, not a difficulty ranking.
Learn how to build reliable AI agents for high-stakes environments. We cover the AI Engineering Loop: tracing, dataset curation, and integrating automated evaluation (Evals) into your CI/CD pipeline to prevent regressions.
Read article
Trusting AI agents requires more than just code. WorkOS demonstrates how to build robust verification loops using CLI health checks, browser snapshots, and LLM judges. Learn to make your agents self-correct and prove their work is done.
Read article
Evals are running tests on traces. They are essential because agents are non-deterministic, meaning the same prompt can give different answers. This process allows teams to measure reliability and prove that changes actually improve the agent.
Read article
Can you use the same LLM for both your agent and your judge? The answer is usually yes. Focus on achieving strong alignment with human labels, as this is the most critical factor for successful AI product evaluation.
Read article
Building trustworthy AI agents requires more than just an answer. The product must be designed to show its work, providing full data provenance, SQL queries, and intermediate calculations for expert verification.
Read article
Building reliable AI agents requires a comprehensive evaluation loop that extends far beyond prompt engineering. The process involves optimizing foundational tools, establishing rigorous evaluation workflows (Evals), and iteratively refining behavior using feedback signals.
Read article