LogoLogo
Python SDKSlack
  • Documentation
  • Cookbooks
  • Self-Hosting
  • Release Notes
  • Reference
  • Arize AI
  • Quickstarts
  • ✨Arize Copilot
  • Arize AI for Agents
  • Concepts
    • Agent Evaluation
    • Tracing
      • What is OpenTelemetry?
      • What is OpenInference?
      • Openinference Semantic Conventions
    • Evaluation
  • 🧪Develop
    • Quickstart: Experiments
    • Datasets
      • Create a dataset
      • Update a dataset
      • Export a dataset
    • Experiments
      • Run experiments
      • Run experiments with code
        • Experiments SDK differences in AX vs Phoenix
        • Log experiment results via SDK
      • Evaluate experiments
      • Evaluate experiment with code
      • CI/CD with experiments
        • Github Action Basics
        • Gitlab CI/CD Basics
      • Download experiment
    • Prompt Playground
      • Use tool calling
      • Use image inputs
    • Playground Integrations
      • OpenAI
      • Azure OpenAI
      • AWS Bedrock
      • VertexAI
      • Custom LLM Models
    • Prompt Hub
  • 🧠Evaluate
    • Online Evals
      • Run evaluations in the UI
      • Run evaluations with code
      • Test LLM evaluator in playground
      • View task details & logs
      • ✨Copilot: Eval Builder
      • ✨Copilot: Eval Analysis
      • ✨Copilot: RAG Analysis
    • Experiment Evals
    • LLM as a Judge
      • Custom Eval Templates
      • Arize Templates
        • Agent Tool Calling
        • Agent Tool Selection
        • Agent Parameter Extraction
        • Agent Path Convergence
        • Agent Planning
        • Agent Reflection
        • Hallucinations
        • Q&A on Retrieved Data
        • Summarization
        • Code Generation
        • Toxicity
        • AI vs Human (Groundtruth)
        • Citation
        • User Frustration
        • SQL Generation
    • Code Evaluations
    • Human Annotations
  • 🔭Observe
    • Quickstart: Tracing
    • Tracing
      • Setup tracing
      • Trace manually
        • Trace inputs and outputs
        • Trace function calls
        • Trace LLM, Retriever and Tool Spans
        • Trace prompt templates & variables
        • Trace as Inferences
        • Send Traces from Phoenix -> Arize
        • Advanced Tracing (OTEL) Examples
      • Add metadata
        • Add events, exceptions and status
        • Add attributes, metadata and tags
        • Send data to a specific project
        • Get the current span context and tracer
      • Configure tracing options
        • Configure OTEL tracer
        • Mask span attributes
        • Redact sensitive data from traces
        • Instrument with OpenInference helpers
      • Query traces
        • Filter Traces
          • Time Filtering
        • Export Traces
        • ✨AI Powered Search & Filter
        • ✨AI Powered Trace Analysis
        • ✨AI Span Analysis & Evaluation
    • Tracing Integrations
      • OpenAI
      • OpenAI Agents SDK
      • LlamaIndex
      • LlamaIndex Workflows
      • LangChain
      • LangGraph
      • Hugging Face smolagents
      • Autogen
      • Google GenAI (Gemini)
      • Model Context Protocol (MCP)
      • Vertex AI
      • Amazon Bedrock
      • Amazon Bedrock Agents
      • MistralAI
      • Anthropic
      • LangFlow
      • Haystack
      • LiteLLM
      • CrewAI
      • Groq
      • DSPy
      • Guardrails AI
      • Prompt flow
      • Vercel AI SDK
      • Llama
      • Together AI
      • OpenTelemetry (arize-otel)
      • BeeAI
    • Evals on Traces
    • Guardrails
    • Sessions
    • Dashboards
      • Dashboard Widgets
      • Tracking Token Usage
      • ✨Copilot: Dashboard Widget Creation
    • Monitors
      • Integrations: Monitors
        • Slack
          • Manual Setup
        • OpsGenie
        • PagerDuty
      • LLM Red Teaming
    • Custom Metrics & Analytics
      • Arize Query Language Syntax
        • Conditionals and Filters
        • All Operators
        • All Functions
      • Custom Metric Examples
      • ✨Copilot: ArizeQL Generator
  • 📈Machine Learning
    • Machine Learning
      • User Guide: ML
      • Quickstart: ML
      • Concepts: ML
        • What Is A Model Schema
        • Delayed Actuals and Tags
        • ML Glossary
      • How To: ML
        • Upload Data to Arize
          • Pandas SDK Example
          • Local File Upload
            • File Upload FAQ
          • Table Ingestion Tuning
          • Wildcard Paths for Cloud Storage
          • Troubleshoot Data Upload
          • Sending Data FAQ
        • Monitors
          • ML Monitor Types
          • Configure Monitors
            • Notifications Providers
          • Programmatically Create Monitors
          • Best Practices for Monitors
        • Dashboards
          • Dashboard Widgets
          • Dashboard Templates
            • Model Performance
            • Pre-Production Performance
            • Feature Analysis
            • Drift
          • Programmatically Create Dashboards
        • Performance Tracing
          • Time Filtering
          • ✨Copilot: Performance Insights
        • Drift Tracing
          • ✨Copilot: Drift Insights
          • Data Distribution Visualization
          • Embeddings for Tabular Data (Multivariate Drift)
        • Custom Metrics
          • Arize Query Language Syntax
            • Conditionals and Filters
            • All Operators
            • All Functions
          • Custom Metric Examples
          • Custom Metrics Query Language
          • ✨Copilot: ArizeQL Generator
        • Troubleshoot Data Quality
          • ✨Copilot: Data Quality Insights
        • Explainability
          • Interpreting & Analyzing Feature Importance Values
          • SHAP
          • Surrogate Model
          • Explainability FAQ
          • Model Explainability
        • Bias Tracing (Fairness)
        • Export Data to Notebook
        • Automate Model Retraining
        • ML FAQ
      • Use Cases: ML
        • Binary Classification
          • Fraud
          • Insurance
        • Multi-Class Classification
        • Regression
          • Lending
          • Customer Lifetime Value
          • Click-Through Rate
        • Timeseries Forecasting
          • Demand Forecasting
          • Churn Forecasting
        • Ranking
          • Collaborative Filtering
          • Search Ranking
        • Natural Language Processing (NLP)
        • Common Industry Use Cases
      • Integrations: ML
        • Google BigQuery
          • GBQ Views
          • Google BigQuery FAQ
        • Snowflake
          • Snowflake Permissions Configuration
        • Databricks
        • Google Cloud Storage (GCS)
        • Azure Blob Storage
        • AWS S3
          • Private Image Link Access Via AWS S3
        • Kafka
        • Airflow Retrain
        • Amazon EventBridge Retrain
        • MLOps Partners
          • Algorithmia
          • Anyscale
          • Azure & Databricks
          • BentoML
          • CML (DVC)
          • Deepnote
          • Feast
          • Google Cloud ML
          • Hugging Face
          • LangChain 🦜🔗
          • MLflow
          • Neptune
          • Paperspace
          • PySpark
          • Ray Serve (Anyscale)
          • SageMaker
            • Batch
            • RealTime
            • Notebook Instance with Greater than 20GB of Data
          • Spell
          • UbiOps
          • Weights & Biases
      • API Reference: ML
        • Python SDK
          • Pandas Batch Logging
            • Client
            • log
            • Schema
            • TypedColumns
            • EmbeddingColumnNames
            • ObjectDetectionColumnNames
            • PromptTemplateColumnNames
            • LLMConfigColumnNames
            • LLMRunMetadataColumnNames
            • NLP_Metrics
            • AutoEmbeddings
            • utils.types.ModelTypes
            • utils.types.Metrics
            • utils.types.Environments
          • Single Record Logging
            • Client
            • log
            • TypedValue
            • Ranking
            • Multi-Class
            • Object Detection
            • Embedding
            • LLMRunMetadata
            • utils.types.ModelTypes
            • utils.types.Metrics
            • utils.types.Environments
        • Java SDK
          • Constructor
          • log
          • bulkLog
          • logValidationRecords
          • logTrainingRecords
        • R SDK
          • Client$new()
          • Client$log()
        • Rest API
    • Computer Vision
      • How to: CV
        • Generate Embeddings
          • How to Generate Your Own Embedding
          • Let Arize Generate Your Embeddings
        • Embedding & Cluster Analyzer
        • ✨Copilot: Embedding Summarization
        • Similarity Search
        • Embedding Drift
        • Embeddings FAQ
      • Integrations: CV
      • Use Cases: CV
        • Image Classification
        • Image Segmentation
        • Object Detection
      • API Reference: CV
Powered by GitBook

Support

  • Chat Us On Slack
  • support@arize.com

Get Started

  • Signup For Free
  • Book A Demo

Copyright © 2025 Arize AI, Inc

On this page
  • Citation Eval Template
  • How to Run
  • Benchmark Results

Was this helpful?

  1. Evaluate
  2. LLM as a Judge
  3. Arize Templates

Citation

Last updated 5 months ago

Was this helpful?

In chatbots and Q&A systems, many times reference links are provided in the response, along with an answer, to help point users to documentation or pages that contain more information or the source for the answer.

EXAMPLE: Q&A from Arize-Phoenix Documentation

QUESTION: What other models does Arize Phoenix support beyond OpenAI for running Evals?

ANSWER: Phoenix does support a large set of LLM models through the model object. Phoenix supports OpenAI (GPT-4, GPT-4-32k, GPT-3.5 Turbo, GPT-3.5 Instruct, etc...), Azure OpenAI, Google Palm2 Text Bison, and All AWS Bedrock models (Claude, Mistral, etc...).

REFERENCE LINK:

This Eval checks the reference link returned answers the question asked in a conversation

Citation Eval Template

You are given a conversation that contains questions by a CUSTOMER and you are trying
to determine if the documentation page shared by the ASSISTANT correctly answers
the CUSTOMERS questions. We will give you the conversation between the customer
and the ASSISTANT and the text of the documentation returned:
    [CONVERSATION AND QUESTION]:
    {conversation}
    ************
    [DOCUMENTATION URL TEXT]:
    {document_text}
    [DOCUMENTATION URL TEXT]:
You should respond "correct" if the documentation text answers the question the
CUSTOMER had in the conversation. If the documentation roughly answers the question
even in a general way the please answer "correct". If there are multiple questions and a single
question is answered, please still answer "correct". If the text does not answer the
question in the conversation, or doesn't contain information that would allow you
to answer the specific question please answer "incorrect".

How to Run

from phoenix.evals import (
    REF_LINK_EVAL_PROMPT_RAILS_MAP,
    REF_LINK_EVAL_PROMPT_TEMPLATE_STR,
    OpenAIModel,
    download_benchmark_dataset,
    llm_classify,
)

model = OpenAIModel(
    model_name="gpt-4",
    temperature=0.0,
)

#The rails is used to hold the output to specific values based on the template
#It will remove text such as ",,," or "..."
#Will ensure the binary value expected from the template is returned
rails = list(REF_LINK_EVAL_PROMPT_RAILS_MAP.values())
relevance_classifications = llm_classify(
    dataframe=df,
    template=REF_LINK_EVAL_PROMPT_TEMPLATE_STR,
    model=model,
    rails=rails,
    provide_explanation=True, #optional to generate explanations for the value produced by the eval LLM
)

Benchmark Results

GPT-4 Results

GPT-3.5

GPT-4 Turbo

Eval
GPT-4o
GPT-4
GPT-4 Turbo
Gemini Pro
GPT-3.5
Claude V2
Palm 2

Precision

0.96

0.97

0.94

0.77

0.89

0.74

0.68

Recall

0.79

0.83

0.69

0.97

0.43

0.48

0.98

F1

0.87

0.89

0.79

0.86

0.58

0.58

0.80

🧠
https://docs.arize.com/phoenix/api/evaluation-models