Default Metrics

Our default metrics cover common evaluation scenarios and are optimized for immediate use.

Metric	Description	Scale	When to use	Requires
`atla_default_conciseness`	How concise and to-the-point the LLM’s response is.	Likert (1-5)	When you want to evaluate whether responses are brief and efficient.	`model_input`, `model_output`
`atla_default_correctness`	How factually accurate the LLM’s response is.	Binary	When you want to check whether responses contain correct information.	`model_input`, `model_output`, `expected_model_output`
`atla_default_faithfulness`	How faithful the LLM is to the provided context.	Likert (1-5)	When you want to check for hallucinations.	`model_input`, `model_output`, `model_context`
`atla_default_helpfulness`	How effectively the LLM’s response addresses the user’s needs.	Likert (1-5)	When you want to assess practical value to users.	`model_input`, `model_output`
`atla_default_logical_coherence`	How well-reasoned and internally consistent the response is.	Likert (1-5)	When you want to check whether responses follow logical reasoning.	`model_input`, `model_output`
`atla_default_relevance`	How well the response addresses the specific query or context.	Likert (1-5)	When you want to ensure responses stay on topic.	`model_input`, `model_output`

Scoring Scales

Our evaluator models produce scores that indicate how well an LLM interaction matches a specific metric.

The interpretation of our default metric scales is as follows:

0	1
Failure or Incorrect	Success or Correct

1	2	3	4	5
Very Poor	Poor	Acceptable	Good	Excellent

Understanding these scales helps you interpret evaluation results and compare performance across different prompts and models.