List of Evaluators

LangWatch offers an extensive library of evaluators to help you evaluate the quality and guarantee the safety of your LLM apps.

Evaluators List

Evaluator	Description
Exact Match Evaluator	A simple evaluator that checks if the output matches the expected_output exactly.
LLM Answer Match	Uses an LLM to check if the generated output answers a question correctly the same way as the expected output, even if their style is different.
LLM Factual Match	Computes with an LLM how factually similar the generated answer is to the expected output.
SQL Query Equivalence	Checks if the SQL query is equivalent to a reference one by using an LLM to infer if it would generate the same results given the table schemas.
ROUGE Score	Traditional NLP metric. ROUGE score for evaluating the similarity between two strings.
BLEU Score	Traditional NLP metric. BLEU score for evaluating the similarity between two strings.

Evaluator	Description
LLM-as-a-Judge Boolean Evaluator	Use an LLM as a judge with a custom prompt to do a true/false boolean evaluation of the message.
LLM-as-a-Judge Score Evaluator	Use an LLM as a judge with custom prompt to do a numeric score evaluation of the message.
LLM-as-a-Judge Category Evaluator	Use an LLM as a judge with a custom prompt to classify the message into custom defined categories.
Rubrics Based Scoring	Rubric-based evaluation metric that is used to evaluate responses. The rubric consists of descriptions for each score, typically ranging from 1 to 5

Evaluator	Description
Ragas Faithfulness	This evaluator assesses the extent to which the generated answer is consistent with the provided context. Higher scores indicate better faithfulness to the context, useful for detecting hallucinations.
Ragas Response Relevancy	Evaluates how pertinent the generated answer is to the given prompt. Higher scores indicate better relevancy.
Ragas Response Context Recall	Uses an LLM to measure how many of relevant documents attributable the claims in the output were successfully retrieved in order to generate an expected output.
Ragas Response Context Precision	Uses an LLM to measure the proportion of chunks in the retrieved context that were relevant to generate the output or the expected output.
Context F1	Balances between precision and recall for context retrieval, increasing it means a better signal-to-noise ratio. Uses traditional string distance metrics.
Context Precision	Measures how accurate is the retrieval compared to expected contexts, increasing it means less noise in the retrieval. Uses traditional string distance metrics.
Context Recall	Measures how many relevant contexts were retrieved compared to expected contexts, increasing it means more signal in the retrieval. Uses traditional string distance metrics.

Evaluator	Description
Lingua Language Detection	This evaluator detects the language of the input and output text to check for example if the generated answer is in the same language as the prompt, or if it’s in a specific expected language.
Summarization Score	Measures how well the summary captures important information from the retrieved contexts.
Valid Format Evaluator	Allows you to check if the output is a valid json, markdown, python, sql, etc. For JSON, can optionally validate against a provided schema.

Evaluator	Description
PII Detection	Detects personally identifiable information in text, including phone numbers, email addresses, and social security numbers. It allows customization of the detection threshold and the specific types of PII to check.
Prompt Injection / Jailbreak Detection	Detect prompt injection attempts and jailbreak attempts in the input
Content Safety	Detect potentially unsafe content in text, including hate speech, self-harm, sexual content, and violence. It allows customization of the severity threshold and the specific categories to check.

Set up your first evaluation using the Evaluation Wizard:

If you have a custom evaluator built in-house, you can follow the guide below to integrate.