# Use Phoenix Evaluators The following are simple functions on top of the LLM Evals building blocks that are pre-tested with benchmark data. {% hint style="info" %} All evals templates are tested against golden data that are available as part of the LLM eval library's [benchmarked data](./#how-we-benchmark-pre-tested-evals) and target precision at 70-90% and F1 at 70-85%. {% endhint %}


Retrieval Eval	RAG individual retrieval	Tested on:	MS Marco, WikiQA	retrieval-rag-relevance.md
Hallucination Eval	Hallucinations on answers to public and private data	Tested on:	Hallucination QA Dataset, Hallucination RAG Dataset	hallucinations.md
Toxicity Eval	Is the AI response racist, biased or toxic	Tested on:	WikiToxic	toxicity.md
Q&A Eval	Private data Q&A Eval	Tested on:	WikiQA	q-and-a-on-retrieved-data.md
Summarization Eval	Summarization performance	Tested on:	GigaWorld, CNNDM, Xsum	summarization-eval.md
Code Generation Eval	Code writing correctness and readability	Tested on:	WikiSQL, HumanEval, CodeXGlu	code-generation-eval.md

## Supported Models. The models are instantiated and usable in the LLM Eval function. The models are also directly callable with strings. ```python model = OpenAIModel(model_name="gpt-4",temperature=0.6) model("What is the largest costal city in France?") ``` We currently support a growing set of models for LLM Evals, please check out the [API section for usage](../../../api/evaluation-models.md).

Model	Support
GPT-4	✔
GPT-3.5 Turbo	✔
GPT-3.5 Instruct	✔
Azure Hosted Open AI	✔
Palm 2 Vertex	✔
AWS Bedrock	✔
Litellm	✔
Huggingface Llama7B	(use litellm)
Anthropic	✔
Cohere	(use litellm)

## How we benchmark pre-tested evals The above diagram shows examples of different environments the Eval harness is desinged to run. The benchmarking environment is designed to enable the testing of the Eval model & Eval template performance against a designed set of data. The above approach allows us to compare models easily in an understandable format:

Hallucination Eval	GPT-4	GPT-3.5
Precision	0.94	0.94
Recall	0.75	0.71
F1	0.83	0.81