Selecting Enrichments

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
Custom LLM Classifier	Custom	This enrichment uses an LLM to classify data based on a user-defined prompt template and list of categories.	custom_llm_classifier	Yes	Fiddler-hosted Llama
Faithfulness	Hallucination	This enrichment identifies the accuracy and reliability of facts presented in AI-generated texts	faithfulness	Yes	OpenAI
Fast Faithfulness	Hallucination	This enrichment identifies the accuracy and reliability of facts presented in AI-generated texts. It is generated by Fiddler's Fast Trust Models	ftl_response_faithfulness	Yes	Fiddler Fast Trust Model
Answer Relevance	Hallucination	This enrichment measures the pertinence of AI-generated responses to their inputs	answer_relevance	Yes	OpenAI
Conciseness	Hallucination	This enrichment evaluates the brevity and clarity of AI-generated responses	conciseness	Yes	OpenAI
Coherence	Hallucination	This enrichment assesses the logical flow and clarity of AI-generated responses	coherence	Yes	OpenAI
Toxicity	Safety	This enrichment classifies whether a piece of text is toxic or not	toxicity	Yes	OpenAI
Fast Jailbreak	Safety	This enrichment detects the presence of jailbreak attempts by the user. It is generated by Fiddler's Fast Trust Models.	ftl_prompt_safety	Yes	Fiddler Fast Trust Model
Fast Safety	Safety	This enrichment generates 10 different safety metrics to measure texts upon. These metrics are: illegal, hateful, harassing, racist, sexist, violent, sexual, harmful, unethical, jailbreaking	ftl_prompt_safety	Yes	Fiddler Fast Trust Model
PII	Safety	This enrichment flags the presence of sensitive information within textual data	pii	No
Regex Match	Safety	This enrichment compares the text with a regular expression string	regex_match	No
Topic	Safety	This enrichment classifies the text into several preset dimensions using a zero-shot classifier	topic_model	No
Banned Keywords	Safety	This enrichment detects the presence of banned keywords configured by the user	banned_keywords	No
Profanity	Safety	This enrichment flags the use of offensive or inappropriate language	profanity	No
Language Detection	Safety	This enrichment identifies the language of the source text	language_detection	No
Evaluate	Text Statistics	This enrichment provides classic text evaluation methods such as BLEU, ROUGE, and Meteor	evaluate	No
Sentiment	Text Statistics	This enrichment provides sentiment analysis of the target text	sentiment	No
TextStat	Text Statistics	This enrichment provides various text statistics such as character/letter count, flesh kinkaid, and others	textstat	No
Token Count	Text Statistics	The Token Count enrichment is designed to count the number of tokens in a string.	token_count	No
SQLValidation	Text Validation	Evaluates different query dialects for syntax correctness.	sql_validation	No
JSONValidation	Text Validation	Validates JSON for correctness and optionally against a user-defined schema.	json_validation	No

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