Evals & Specs

Two powerful tools to measure and optimize your AI systems

Evals vs Specs

Kiln has two powerful features to ensure your AI systems perform as expected, drive optimizations and don't regress in quality:

• Evals: Build industry standard evals with methods like LLM-as-Judge and G-Eval.

• Specs: A Kiln spec includes an eval, but adds synthetic evaluation data generation, edge case detection, judge prompt generation, and more. It's an easy, fast and more comprehensive way to build evals.

	Kiln Evals	Kiln Specs
LLM-as-Judge including G-Eval	✅	✅
Judge Prompt Creation	Manual	Automatic
Edge Case Discovery	Manual	Automatic
Eval Data Creation	Manual With synthetic tooling	Automatic
Eval Accuracy	Variable	High Human in the loop validation and refinement
Approx. Effort	30 mins+	5-10mins
Needed Expertise	Data Science Basics Understand Golden sets, data labeling	No experience necessary Fully Guided UI
Kiln Account	Optional	Required
Docs	Evals Guide	Specs Guide

Guides

• Specs Guide: build an eval, synthetic data, and align your judge in one interactive flow

• Evals 101: build your first eval start to finish

• Many Small Evals Beat One Big Eval: Blog post which walks through how to setup eval tooling, and how to create an eval culture on your team.

• Evaluate RAG Accuracy: Kiln can generate custom Q&A evals which test your RAG with knowledge from your documents

• Evaluate Tool Use: ensure your agents are using the right tools, at the right time, with the right parameters with tool use evals

• Use Kiln Evals on External Agents: If you've built agents in another platform, you can still evaluate them in Kiln using our MCP connectors.