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
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. |
| 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 |
- 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. |
- 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 LLM
from phoenix.evals.metrics import FaithfulnessEvaluator
# Initialize the LLM client
llm = LLM(provider="openai", model="gpt-4o")
# Create the evaluator
faithfulness_eval = FaithfulnessEvaluator(llm=llm)
# Inspect the evaluator's requirements
print(faithfulness_eval.describe())
# Evaluate a single example
eval_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 evaluator
const faithfulnessEvaluator = createFaithfulnessEvaluator({
model: openai("gpt-4o"),
});
// Evaluate an example
const 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: "..." }
from phoenix.evals import LLM
from phoenix.evals.metrics import FaithfulnessEvaluator
llm = LLM(provider="openai", model="gpt-4o")
faithfulness_eval = FaithfulnessEvaluator(llm=llm)
# Example with nested data and multiple documents
eval_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 documents
input_mapping = {
"input": "input.query",
"output": "output.response",
"context": lambda x: "\n\n".join(x["retrieved"]["documents"])
}
scores = faithfulness_eval.evaluate(eval_input, 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 names
const 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."
],
});
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 FaithfulnessEvaluator
from phoenix.evals import LLM, ClassificationEvaluator
llm = LLM(provider="openai", model="gpt-4o")
evaluator = FaithfulnessEvaluator(llm=llm)
# View the prompt template
print(evaluator.prompt_template)
# Create a custom evaluator based on the built-in template
custom_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 template
console.log(FAITHFULNESS_CLASSIFICATION_EVALUATOR_CONFIG.template);
// Create a custom evaluator with a modified template
const 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:
API Reference
