# Evals & Specs #### 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**](https://docs.kiln.tech/docs/evals-and-specs/evaluations): Build industry standard evals with methods like LLM-as-Judge and G-Eval. * [**Specs**](https://docs.kiln.tech/docs/evals-and-specs/specifications)**:** 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 EvalsKiln Specs

LLM-as-Judge

including G-Eval

✅✅
Judge Prompt CreationManualAutomatic
Edge Case DiscoveryManualAutomatic
Eval Data Creation

Manual

With synthetic tooling

Automatic
Eval AccuracyVariable

High

Human in the loop validation and refinement

Approx. Effort30 mins+5-10mins
Needed ExpertiseData Science Basics
Understand Golden sets, data labeling		No experience necessary
Fully Guided UI
Kiln AccountOptionalRequired
DocsEvals GuideSpecs Guide
#### Guides * [Specs Guide](https://docs.kiln.tech/docs/evals-and-specs/specifications): build an eval, synthetic data, and align your judge in one interactive flow * [Evals 101](https://docs.kiln.tech/docs/evals-and-specs/evaluations): build your first eval start to finish * [Many Small Evals Beat One Big Eval](https://kiln.tech/blog/you_need_many_small_evals_for_ai_products): Blog post which walks through how to setup eval tooling, and how to create an eval culture on your team. * [Evaluate RAG Accuracy](https://docs.kiln.tech/docs/evals-and-specs/evaluate-rag-accuracy-q-and-a-evals): Kiln can generate custom Q\&A evals which test your RAG with knowledge from your documents * [Evaluate Tool Use](https://docs.kiln.tech/docs/evals-and-specs/evaluate-appropriate-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](https://docs.kiln.tech/docs/tools-and-mcp/connect-to-existing-agents): If you've built agents in another platform, you can still evaluate them in Kiln using our MCP connectors. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.kiln.tech/docs/evals-and-specs.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.