claude-code-skills-reference/promptfoo-evaluation/SKILL.md

name, description

name	description
promptfoo-evaluation	Configures and runs LLM evaluation using Promptfoo framework. Use when setting up prompt testing, creating evaluation configs (promptfooconfig.yaml), writing Python custom assertions, implementing llm-rubric for LLM-as-judge, or managing few-shot examples in prompts. Triggers on keywords like "promptfoo", "eval", "LLM evaluation", "prompt testing", or "model comparison".

Promptfoo Evaluation

Overview

This skill provides guidance for configuring and running LLM evaluations using Promptfoo, an open-source CLI tool for testing and comparing LLM outputs.

Quick Start

# Initialize a new evaluation project
npx promptfoo@latest init

# Run evaluation
npx promptfoo@latest eval

# View results in browser
npx promptfoo@latest view

Configuration Structure

A typical Promptfoo project structure:

project/
├── promptfooconfig.yaml    # Main configuration
├── prompts/
│   ├── system.md           # System prompt
│   └── chat.json           # Chat format prompt
├── tests/
│   └── cases.yaml          # Test cases
└── scripts/
    └── metrics.py          # Custom Python assertions

Core Configuration (promptfooconfig.yaml)

# yaml-language-server: $schema=https://promptfoo.dev/config-schema.json
description: "My LLM Evaluation"

# Prompts to test
prompts:
  - file://prompts/system.md
  - file://prompts/chat.json

# Models to compare
providers:
  - id: anthropic:messages:claude-sonnet-4-5-20250929
    label: Claude-4.5-Sonnet
  - id: openai:gpt-4.1
    label: GPT-4.1

# Test cases
tests: file://tests/cases.yaml

# Default assertions for all tests
defaultTest:
  assert:
    - type: python
      value: file://scripts/metrics.py:custom_assert
    - type: llm-rubric
      value: |
        Evaluate the response quality on a 0-1 scale.
      threshold: 0.7

# Output path
outputPath: results/eval-results.json

Prompt Formats

Text Prompt (system.md)

You are a helpful assistant.

Task: {{task}}
Context: {{context}}

Chat Format (chat.json)

[
  {"role": "system", "content": "{{system_prompt}}"},
  {"role": "user", "content": "{{user_input}}"}
]

Few-Shot Pattern

Embed examples directly in prompt or use chat format with assistant messages:

[
  {"role": "system", "content": "{{system_prompt}}"},
  {"role": "user", "content": "Example input: {{example_input}}"},
  {"role": "assistant", "content": "{{example_output}}"},
  {"role": "user", "content": "Now process: {{actual_input}}"}
]

Test Cases (tests/cases.yaml)

- description: "Test case 1"
  vars:
    system_prompt: file://prompts/system.md
    user_input: "Hello world"
    # Load content from files
    context: file://data/context.txt
  assert:
    - type: contains
      value: "expected text"
    - type: python
      value: file://scripts/metrics.py:custom_check
      threshold: 0.8

Python Custom Assertions

Create a Python file for custom assertions (e.g., scripts/metrics.py):

def get_assert(output: str, context: dict) -> dict:
    """Default assertion function."""
    vars_dict = context.get('vars', {})

    # Access test variables
    expected = vars_dict.get('expected', '')

    # Return result
    return {
        "pass": expected in output,
        "score": 0.8,
        "reason": "Contains expected content",
        "named_scores": {"relevance": 0.9}
    }

def custom_check(output: str, context: dict) -> dict:
    """Custom named assertion."""
    word_count = len(output.split())
    passed = 100 <= word_count <= 500

    return {
        "pass": passed,
        "score": min(1.0, word_count / 300),
        "reason": f"Word count: {word_count}"
    }

Key points:

• Default function name is get_assert
• Specify function with file://path.py:function_name
• Return bool, float (score), or dict with pass/score/reason
• Access variables via context['vars']

LLM-as-Judge (llm-rubric)

assert:
  - type: llm-rubric
    value: |
      Evaluate the response based on:
      1. Accuracy of information
      2. Clarity of explanation
      3. Completeness

      Score 0.0-1.0 where 0.7+ is passing.
    threshold: 0.7
    provider: openai:gpt-4.1  # Optional: override grader model

Best practices:

• Provide clear scoring criteria
• Use threshold to set minimum passing score
• Default grader uses available API keys (OpenAI → Anthropic → Google)

Common Assertion Types

Type	Usage	Example
contains	Check substring	value: "hello"
icontains	Case-insensitive	value: "HELLO"
equals	Exact match	value: "42"
regex	Pattern match	value: "\\d{4}"
python	Custom logic	value: file://script.py
llm-rubric	LLM grading	value: "Is professional"
latency	Response time	threshold: 1000

File References

All paths are relative to config file location:

# Load file content as variable
vars:
  content: file://data/input.txt

# Load prompt from file
prompts:
  - file://prompts/main.md

# Load test cases from file
tests: file://tests/cases.yaml

# Load Python assertion
assert:
  - type: python
    value: file://scripts/check.py:validate

Running Evaluations

# Basic run
npx promptfoo@latest eval

# With specific config
npx promptfoo@latest eval --config path/to/config.yaml

# Output to file
npx promptfoo@latest eval --output results.json

# Filter tests
npx promptfoo@latest eval --filter-metadata category=math

# View results
npx promptfoo@latest view

Troubleshooting

Python not found:

export PROMPTFOO_PYTHON=python3

Large outputs truncated: Outputs over 30000 characters are truncated. Use head_limit in assertions.

File not found errors: Ensure paths are relative to promptfooconfig.yaml location.

Echo Provider (Preview Mode)

Use the echo provider to preview rendered prompts without making API calls:

# promptfooconfig-preview.yaml
providers:
  - echo  # Returns prompt as output, no API calls

tests:
  - vars:
      input: "test content"

Use cases:

• Preview prompt rendering before expensive API calls
• Verify Few-shot examples are loaded correctly
• Debug variable substitution issues
• Validate prompt structure

# Run preview mode
npx promptfoo@latest eval --config promptfooconfig-preview.yaml

Cost: Free - no API tokens consumed.

Advanced Few-Shot Implementation

Multi-turn Conversation Pattern

For complex few-shot learning with full examples:

[
  {"role": "system", "content": "{{system_prompt}}"},

  // Few-shot Example 1
  {"role": "user", "content": "Task: {{example_input_1}}"},
  {"role": "assistant", "content": "{{example_output_1}}"},

  // Few-shot Example 2 (optional)
  {"role": "user", "content": "Task: {{example_input_2}}"},
  {"role": "assistant", "content": "{{example_output_2}}"},

  // Actual test
  {"role": "user", "content": "Task: {{actual_input}}"}
]

Test case configuration:

tests:
  - vars:
      system_prompt: file://prompts/system.md
      # Few-shot examples
      example_input_1: file://data/examples/input1.txt
      example_output_1: file://data/examples/output1.txt
      example_input_2: file://data/examples/input2.txt
      example_output_2: file://data/examples/output2.txt
      # Actual test
      actual_input: file://data/test1.txt

Best practices:

• Use 1-3 few-shot examples (more may dilute effectiveness)
• Ensure examples match the task format exactly
• Load examples from files for better maintainability
• Use echo provider first to verify structure

Long Text Handling

For Chinese/long-form content evaluations (10k+ characters):

Configuration:

providers:
  - id: anthropic:messages:claude-sonnet-4-5-20250929
    config:
      max_tokens: 8192  # Increase for long outputs

defaultTest:
  assert:
    - type: python
      value: file://scripts/metrics.py:check_length

Python assertion for text metrics:

import re

def strip_tags(text: str) -> str:
    """Remove HTML tags for pure text."""
    return re.sub(r'<[^>]+>', '', text)

def check_length(output: str, context: dict) -> dict:
    """Check output length constraints."""
    raw_input = context['vars'].get('raw_input', '')

    input_len = len(strip_tags(raw_input))
    output_len = len(strip_tags(output))

    reduction_ratio = 1 - (output_len / input_len) if input_len > 0 else 0

    return {
        "pass": 0.7 <= reduction_ratio <= 0.9,
        "score": reduction_ratio,
        "reason": f"Reduction: {reduction_ratio:.1%} (target: 70-90%)",
        "named_scores": {
            "input_length": input_len,
            "output_length": output_len,
            "reduction_ratio": reduction_ratio
        }
    }

Real-World Example

Project: Chinese short-video content curation from long transcripts

Structure:

tiaogaoren/
├── promptfooconfig.yaml          # Production config
├── promptfooconfig-preview.yaml  # Preview config (echo provider)
├── prompts/
│   ├── tiaogaoren-prompt.json   # Chat format with few-shot
│   └── v4/system-v4.md          # System prompt
├── tests/cases.yaml              # 3 test samples
├── scripts/metrics.py            # Custom metrics (reduction ratio, etc.)
├── data/                         # 5 samples (2 few-shot, 3 eval)
└── results/

See: ~/workspace/prompts/tiaogaoren/ for full implementation.

Resources

For detailed API reference and advanced patterns, see references/promptfoo_api.md.