LLM Evaluation
List of Evaluators
Find the evaluator for your use case
LangWatch offers an extensive library of evaluators to help you evaluate the quality and guarantee the safety of your LLM apps.
While here you can find a reference list, to get the execution code you can use the Evaluation Wizard on LangWatch platform.
Evaluators List
Expected Answer Evaluation
For when you have the golden answer and want to measure how correct the LLM gets it
Expected Answer Evaluation
For when you have the golden answer and want to measure how correct the LLM gets it
| 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. |
LLM-as-Judge
For when you don't have a golden answer, but have a set of rules for another LLM to evaluate quality
LLM-as-Judge
For when you don't have a golden answer, but have a set of rules for another LLM to evaluate quality
| 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 |
RAG Quality
For measuring the quality of your RAG, check for hallucinations with faithfulness and precision/recall
RAG Quality
For measuring the quality of your RAG, check for hallucinations with faithfulness and precision/recall
| 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. |
Quality Aspects Evaluation
For when you want to check the language, structure, style and other general quality metrics
Quality Aspects Evaluation
For when you want to check the language, structure, style and other general quality 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. |
Safety
Check for PII, prompt injection attempts and toxic content
Safety
Check for PII, prompt injection attempts and toxic content
| 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. |
Running Evaluations
Set up your first evaluation using the Evaluation Wizard: