fernandezbaptiste/promptfoo-evaluation

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: /Users/tiansheng/Workspace/prompts/tiaogaoren/ for full implementation.

Resources

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