> ## Documentation Index
> Fetch the complete documentation index at: https://arize-ax.mintlify.site/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Create and validate evaluators
> Interpret traces, define success criteria, and build code and LLM-as-a-judge evaluators calibrated against human judgment.
Evaluation turns raw traces into measurable judgments of quality. Interpret what your traces reveal, define the criteria that separate a good response from a bad one, and build evaluators, both code-based and LLM-as-a-judge, whose scores you can rely on.
## Before you write evals for agents, read your data | Ep. 5
Read what your traces reveal and establish the criteria that distinguish a good response from a bad one.
## Your first code eval for agents: catch bugs in 5 lines of Python | Ep. 6
Score outputs with deterministic checks for criteria that don't require a model to judge.
## LLM-as-a-judge for agents: build a custom eval rubric that works | Ep. 7
Use an LLM to assess qualities that are difficult to capture in code, and write judge prompts that produce consistent scores.
## 5 LLM and agent eval mistakes that turn metrics into noise | Ep. 8
The common mistakes that make evaluations misleading, and how to avoid them.
## Is your LLM judge right? Calibrate with meta-evaluation | Ep. 9
Calibrate an LLM judge against human judgment so you can rely on its scores.
## Up next
Prove changes with experiments, evaluate production traffic, and turn failures into fixes.