Prompt API Reference

The prompt system in Ragas provides a flexible and type-safe way to define prompts for LLM-based metrics and other components. This page documents the core prompt classes and their usage.

Overview

Ragas uses a modular prompt architecture based on the BasePrompt class. Prompts can be:

Input/Output Models: Pydantic BaseModel classes that define the structure of prompt inputs and outputs
Prompt Classes: Inherit from BasePrompt to define instructions, examples, and prompt generation logic
String Prompts: Simple text-based prompts for backward compatibility

Core Classes

::: ragas.prompt options: members: - BasePrompt - StringPrompt - InputModel - OutputModel - PydanticPrompt - BoolIO - StringIO - PromptMixin

Metrics Collections Prompts

Modern metrics in Ragas use specialized prompt classes. Each metric module contains:

Input Model: Defines what data the prompt needs (e.g., FaithfulnessInput)
Output Model: Defines the expected LLM response structure (e.g., FaithfulnessOutput)
Prompt Class: Inherits from BasePrompt to generate the prompt string with examples and instructions

Example: Faithfulness Metric Prompts

from ragas.metrics.collections.faithfulness.util import (
    FaithfulnessPrompt,
    FaithfulnessInput,
    FaithfulnessOutput,
)

# The prompt class combines input/output models with instructions and examples
prompt = FaithfulnessPrompt()

# Create input data
input_data = FaithfulnessInput(
    response="The capital of France is Paris.",
    context="Paris is the capital and most populous city of France."
)

# Generate the prompt string for the LLM
prompt_string = prompt.to_string(input_data)

# The output will be structured according to FaithfulnessOutput model

Available Metric Prompts

See the individual metric documentation for details on their prompts:

Customization

For detailed guidance on customizing prompts for metrics, see Modifying prompts in metrics.

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