Built-in Evaluators
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| 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. |
| BLEU Score | Traditional NLP metric. BLEU score for evaluating the similarity between two strings. |
| LLM Factual Match | Computes with an LLM how factually similar the generated answer is to the expected output. |
| ROUGE Score | Traditional NLP metric. ROUGE score for evaluating the similarity between two strings. |
| 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. |
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 Category Evaluator | Use an LLM as a judge with a custom prompt to classify the message into custom defined categories. |
| LLM-as-a-Judge Score Evaluator | Use an LLM as a judge with custom prompt to do a numeric score evaluation of the message. |
| 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| Evaluator | Description |
|---|---|
| Ragas Context Precision | This metric evaluates whether all of the ground-truth relevant items present in the contexts are ranked higher or not. Higher scores indicate better precision. |
| Ragas Context Recall | This evaluator measures the extent to which the retrieved context aligns with the annotated answer, treated as the ground truth. Higher values indicate better performance. |
| 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. |
| 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. |
| 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 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. |
| 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 Relevancy | Evaluates how pertinent the generated answer is to the given prompt. Higher scores indicate better relevancy. |
Quality Aspects Evaluation
For when you want to check the language, structure, style and other general quality metrics| Evaluator | Description |
|---|---|
| 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. |
| 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. |
Safety
Check for PII, prompt injection attempts and toxic content| Evaluator | Description |
|---|---|
| Llama Guard | This evaluator is a special version of Llama trained strictly for acting as a guardrail, following customizable guidelines. It can work both as a safety evaluator and as policy enforcement. |
| Azure Content Safety | This evaluator detects 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. |
| Azure Jailbreak Detection | This evaluator checks for jailbreak-attempt in the input using Azure’s Content Safety API. |
| Azure Prompt Shield | This evaluator checks for prompt injection attempt in the input and the contexts using Azure’s Content Safety API. |
| OpenAI Moderation | This evaluator uses OpenAI’s moderation API to detect potentially harmful content in text, including harassment, hate speech, self-harm, sexual content, and violence. |
| Presidio 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. |
Other
Miscellaneous evaluators| Evaluator | Description |
|---|---|
| Custom Basic Evaluator | Allows you to check for simple text matches or regex evaluation. |
| Competitor Blocklist | This evaluator checks if any of the specified competitors was mentioned |
| Competitor Allowlist Check | This evaluator use an LLM-as-judge to check if the conversation is related to competitors, without having to name them explicitly |
| Competitor LLM Check | This evaluator implements LLM-as-a-judge with a function call approach to check if the message contains a mention of a competitor. |
| Off Topic Evaluator | This evaluator checks if the user message is concerning one of the allowed topics of the chatbot |
| Query Resolution | This evaluator checks if all the user queries in the conversation were resolved. Useful to detect when the bot doesn’t know how to answer or can’t help the user. |
| Semantic Similarity Evaluator | Allows you to check for semantic similarity or dissimilarity between input and output and a target value, so you can avoid sentences that you don’t want to be present without having to match on the exact text. |
| Ragas Answer Correctness | Computes with an LLM a weighted combination of factual as well as semantic similarity between the generated answer and the expected output. |
| Ragas Answer Relevancy | Evaluates how pertinent the generated answer is to the given prompt. Higher scores indicate better relevancy. |
| Ragas Context Relevancy | This metric gauges the relevancy of the retrieved context, calculated based on both the question and contexts. The values fall within the range of (0, 1), with higher values indicating better relevancy. |
| Ragas Context Utilization | This metric evaluates whether all of the output relevant items present in the contexts are ranked higher or not. Higher scores indicate better utilization. |
| Example Evaluator | This evaluator serves as a boilerplate for creating new evaluators. |
Running Evaluations
Set up your first evaluation using the Evaluation Wizard: