Faithfulness - Phoenix

Overview#

The Faithfulness evaluator is a specialized hallucination-detection metric that determines whether an LLM’s response is grounded in and faithful to the provided context. It detects when responses contain information that is not supported by or contradicts the reference context.

When to Use#

Use the Faithfulness evaluator when you need to:

Validate RAG (Retrieval-Augmented Generation) outputs - Ensure answers are based on retrieved documents or search results
Detect hallucinations in grounded responses - Identify when the LLM makes up information not present in the context
Evaluate Q&A systems over private data - Verify responses only contain information from your knowledge base

Info

This evaluator is specifically designed for grounded responses where context is provided. It is not designed to validate general world knowledge or facts the LLM learned during training.

Supported Levels#

The level of an evaluator determines the scope of the evaluation in OpenTelemetry terms. Some evaluations are applicable to individual spans, some to full traces or sessions, and some are applicable at multiple levels.

Level	Supported	Notes
Span	Yes	Best for LLM spans with RAG context. Apply to spans where `input`, `output`, and retrieved `context` are available.

Relevant span kinds: LLM spans, particularly those in RAG pipelines where documents are retrieved and used as context.

Input Requirements#

The Faithfulness evaluator requires three inputs:

Field	Type	Description
`input`	`string`	The user’s query or question
`output`	`string`	The LLM’s response to evaluate
`context`	`string`	The reference context or retrieved documents

Formatting Tips#

For best results:

Use human-readable strings rather than raw JSON for all inputs

For multi-turn conversations, format the input as a readable conversation:

User: What is the refund policy?Assistant: You can request a refund within 30 days.User: How do I request one?

For multiple retrieved documents, concatenate them with clear separators (see Input Mapping example below):

Our return policy allows returns within 30 days of purchase.Refunds are processed within 5 business days.Items must be in original condition with tags attached.

Output Interpretation#

The evaluator returns a Score object with the following properties:

Property	Value	Description
`label`	`"faithful"` or `"unfaithful"`	Classification result
`score`	`1.0` or `0.0`	Numeric score (1.0 = faithful, 0.0 = unfaithful)
`explanation`	`string`	LLM-generated reasoning for the classification
`direction`	`"maximize"`	Higher scores are better
`metadata`	`object`	Additional information such as the model name. When tracing is enabled, includes the `trace_id` for the evaluation.

Interpretation:

Faithful (1.0): The response is fully supported by the context and does not contain made-up information
Unfaithful (0.0): The response contains information not present in the context or contradicts it

Usage Examples#

from phoenix.evals import LLMfrom phoenix.evals.metrics import FaithfulnessEvaluator# Initialize the LLM clientllm = LLM(provider="openai", model="gpt-4o")# Create the evaluatorfaithfulness_eval = FaithfulnessEvaluator(llm=llm)# Inspect the evaluator's requirementsprint(faithfulness_eval.describe())# Evaluate a single exampleeval_input = {    "input": "What is the capital of France?",    "output": "Paris is the capital of France.",    "context": "Paris is the capital and largest city of France."}scores = faithfulness_eval.evaluate(eval_input)print(scores[0])# Score(name='faithfulness', score=1.0, label='faithful', ...)

import { createFaithfulnessEvaluator } from "@arizeai/phoenix-evals";import { openai } from "@ai-sdk/openai";// Create the evaluatorconst faithfulnessEvaluator = createFaithfulnessEvaluator({  model: openai("gpt-4o"),});// Evaluate an exampleconst result = await faithfulnessEvaluator.evaluate({  input: "What is the capital of France?",  output: "Paris is the capital of France.",  context: "Paris is the capital and largest city of France.",});console.log(result);// { score: 1, label: "faithful", explanation: "..." }

Using Input Mapping#

When your data has different field names or requires transformation, use input mapping. This is especially useful when you need to combine multiple documents into a single context string.

from phoenix.evals import LLMfrom phoenix.evals.metrics import FaithfulnessEvaluatorllm = LLM(provider="openai", model="gpt-4o")faithfulness_eval = FaithfulnessEvaluator(llm=llm)# Example with nested data and multiple documentseval_input = {    "input": {"query": "What is the return policy?"},    "output": {"response": "You can return items within 30 days."},    "retrieved": {        "documents": [            "Our return policy allows returns within 30 days.",            "Refunds are processed within 5 business days."        ]    }}# Use input mapping with a lambda to concatenate documentsinput_mapping = {    "input": "input.query",    "output": "output.response",    "context": lambda x: "\n\n".join(x["retrieved"]["documents"])}scores = faithfulness_eval.evaluate(eval_input, input_mapping)

For more details on input mapping options, see Input Mapping.

import { bindEvaluator, createFaithfulnessEvaluator } from "@arizeai/phoenix-evals";import { openai } from "@ai-sdk/openai";const faithfulnessEvaluator = createFaithfulnessEvaluator({  model: openai("gpt-4o"),});// Bind with input mapping for different field namesconst boundEvaluator = bindEvaluator(faithfulnessEvaluator, {  inputMapping: {    input: "question",    output: "answer",    context: (data) => data.documents.join("\n\n"),  },});const result = await boundEvaluator.evaluate({  question: "What is the return policy?",  answer: "You can return items within 30 days.",  documents: [    "Our return policy allows returns within 30 days.",    "Refunds are processed within 5 business days."  ],});

For more details on input mapping options, see Input Mapping.

Configuration#

For LLM client configuration options, see Configuring the LLM.

Viewing and Modifying the Prompt#

You can view the latest versions of our prompt templates on GitHub. The evaluators are designed to work well in a variety of contexts, but we highly recommend modifying the prompt to be more specific to your use case. Feel free to adapt them.

from phoenix.evals.metrics import FaithfulnessEvaluatorfrom phoenix.evals import LLM, ClassificationEvaluatorllm = LLM(provider="openai", model="gpt-4o")evaluator = FaithfulnessEvaluator(llm=llm)# View the prompt templateprint(evaluator.prompt_template)# Create a custom evaluator based on the built-in templatecustom_evaluator = ClassificationEvaluator(    name="faithfulness",    prompt_template=evaluator.prompt_template,  # Modify as needed    llm=llm,    choices={"faithful": 1.0, "unfaithful": 0.0},    direction="maximize",)

import { FAITHFULNESS_CLASSIFICATION_EVALUATOR_CONFIG, createFaithfulnessEvaluator } from "@arizeai/phoenix-evals";import { openai } from "@ai-sdk/openai";// View the prompt templateconsole.log(FAITHFULNESS_CLASSIFICATION_EVALUATOR_CONFIG.template);// Create a custom evaluator with a modified templateconst customEvaluator = createFaithfulnessEvaluator({  model: openai("gpt-4o"),  promptTemplate: FAITHFULNESS_CLASSIFICATION_EVALUATOR_CONFIG.template, // Modify as needed});

Using with Phoenix#

Evaluating Traces#

Run evaluations on traces collected in Phoenix and log results as annotations:

Running Experiments#

Use the Faithfulness evaluator in Phoenix experiments:

Using Evaluators in Experiments

API Reference#

Python: FaithfulnessEvaluator
TypeScript: createFaithfulnessEvaluator

Retrieval Relevance Evaluator - Evaluate whether retrieved information is relevant to the request
Correctness Evaluator - Evaluate factual accuracy

Overview#

When to Use#

Supported Levels#

Input Requirements#

Formatting Tips#

Output Interpretation#

Usage Examples#

Using Input Mapping#

Configuration#

Viewing and Modifying the Prompt#

Using with Phoenix#

Evaluating Traces#

Running Experiments#

API Reference#

Related#