> ## Documentation Index > Fetch the complete documentation index at: https://docs.fiddler.ai/llms.txt > Use this file to discover all available pages before exploring further. # Selecting Enrichments > Learn about Fiddler’s enrichments and monitor key aspects of LLM applications. Discover the different factors to analyze for your specific use case. For a complete reference of all LLM enrichments with output columns and sub-metrics, see the [LLM Observability Metrics Reference](/reference/llm-observability-metrics). Fiddler offers enrichments out of the box to monitor different aspects of LLM applications. Use the below table to select the right enrichment for your specific use case. This table provides high level information on the metric, the enrichment to use to measure the metric, if the metric uses LLMs, and if so, what LLM it uses. If you have a use case not covered by the below enrichments out of the box, please contact your administrator. | Metric | Metric Category | Description | Enrichment | LLM Used? | LLM Type | | --------------------------------------------------------------------------------------- | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------- | --------- | ------------ | | [Faithfulness](/observability/llm/enrichments#faithfulness) | Hallucination | This enrichment identifies the accuracy and reliability of facts presented in AI-generated texts | `faithfulness` | Yes | OpenAI | | [Faithfulness (Centor Model)](/observability/llm/enrichments#faithfulness-centor-model) | Hallucination | This enrichment identifies the accuracy and reliability of facts presented in AI-generated texts. It is powered by the Centor Model for Faithfulness | `ftl_response_faithfulness` | Yes | Centor Model | | [Answer Relevance](/observability/llm/enrichments#answer-relevance) | Hallucination | This enrichment measures the pertinence of AI-generated responses to their inputs | `answer_relevance` | Yes | OpenAI | | [Conciseness](/observability/llm/enrichments#conciseness) | Hallucination | This enrichment evaluates the brevity and clarity of AI-generated responses | `conciseness` | Yes | OpenAI | | [Coherence](/observability/llm/enrichments#coherence) | Hallucination | This enrichment assesses the logical flow and clarity of AI-generated responses | `coherence` | Yes | OpenAI | | [Safety](/observability/llm/enrichments#safety) | Safety | This enrichment generates 11 different safety metrics to measure texts upon. These metrics are: `illegal, hateful, harassing, racist, sexist, violent, sexual, harmful, unethical, jailbreaking, roleplaying` | `ftl_prompt_safety` | Yes | Centor Model | | [PII](/observability/llm/enrichments#personally-identifiable-information) | Safety | This enrichment flags the presence of sensitive information within textual data | `pii` | No | | | [Regex Match](/observability/llm/enrichments#regex-match) | Safety | This enrichment compares the text with a regular expression string | `regex_match` | No | | | [Topic](/observability/llm/enrichments#topic) | Safety | This enrichment classifies the text into several preset dimensions using a zero-shot classifier | `topic_model` | No | | | [Banned Keywords](/observability/llm/enrichments#banned-keyword-detector) | Safety | This enrichment detects the presence of banned keywords configured by the user | `banned_keywords` | No | | | [Profanity](/observability/llm/enrichments#profanity) | Safety | This enrichment flags the use of offensive or inappropriate language | `profanity` | No | | | [Language Detection](/observability/llm/enrichments#language-detector) | Safety | This enrichment identifies the language of the source text | `language_detection` | No | | | [Evaluate](/observability/llm/enrichments#evaluate) | Text Statistics | This enrichment provides classic text evaluation methods such as BLEU, ROUGE, and Meteor | `evaluate` | No | | | [Sentiment](/observability/llm/enrichments#sentiment) | Text Statistics | This enrichment provides sentiment analysis of the target text | `sentiment` | No | | | [TextStat](/observability/llm/enrichments#textstat) | Text Statistics | This enrichment provides various text statistics such as character/letter count, Flesch-Kincaid, and others | `textstat` | No | | | [Token Count](/observability/llm/enrichments#token-count) | Text Statistics | The Token Count enrichment is designed to count the number of tokens in a string. | `token_count` | No | | | [SQLValidation](/observability/llm/enrichments#sql-validation) | Text Validation | Evaluates different query dialects for syntax correctness. | `sql_validation` | No | | | [JSONValidation](/observability/llm/enrichments#json-validation) | Text Validation | Validates JSON for correctness and optionally against a user-defined schema. | `json_validation` | No | |