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-4-6
    label: Claude-Sonnet-4.6
  - id: openai:gpt-4.1
    label: GPT-4.1

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

# Concurrency control (MUST be under commandLineOptions, NOT top-level)
commandLineOptions:
  maxConcurrency: 2

# 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

When using a relay/proxy API, each llm-rubric assertion needs its own provider config with apiBaseUrl. Otherwise the grader falls back to the default Anthropic/OpenAI endpoint and gets 401 errors:

assert:
  - type: llm-rubric
    value: |
      Evaluate quality on a 0-1 scale.
    threshold: 0.7
    provider:
      id: anthropic:messages:claude-sonnet-4-6
      config:
        apiBaseUrl: https://your-relay.example.com/api

Best practices:

• Provide clear scoring criteria
• Use threshold to set minimum passing score
• Default grader uses available API keys (OpenAI → Anthropic → Google)
• When using relay/proxy: every llm-rubric must have its own provider with apiBaseUrl — the main provider's apiBaseUrl is NOT inherited

Common Assertion Types

File References

All file:// paths are resolved relative to promptfooconfig.yaml location (NOT the YAML file containing the reference). This is a common gotcha when tests: references a separate YAML file — the file:// paths inside that test file still resolve from the config root.

# 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

Relay / Proxy API Configuration

When using an API relay or proxy instead of direct Anthropic/OpenAI endpoints:

providers:
  - id: anthropic:messages:claude-sonnet-4-6
    label: Claude-Sonnet-4.6
    config:
      max_tokens: 4096
      apiBaseUrl: https://your-relay.example.com/api  # Promptfoo appends /v1/messages

# CRITICAL: maxConcurrency MUST be under commandLineOptions (NOT top-level)
commandLineOptions:
  maxConcurrency: 1  # Respect relay rate limits

Key rules:

• apiBaseUrl goes in providers[].config — Promptfoo appends /v1/messages automatically
• maxConcurrency must be under commandLineOptions: — placing it at top level is silently ignored
• When using relay with LLM-as-judge, set maxConcurrency: 1 to avoid concurrent request limits (generation + grading share the same pool)
• Pass relay token as ANTHROPIC_API_KEY env var

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: All file:// paths resolve relative to promptfooconfig.yaml location.

maxConcurrency ignored (shows "up to N at a time"): maxConcurrency must be under commandLineOptions:, not at the YAML top level. This is a common mistake.

LLM-as-judge returns 401 with relay API: Each llm-rubric assertion must have its own provider with apiBaseUrl. The main provider config is not inherited by grader assertions.

HTML tags in model output inflating metrics: Models may output <br>, <b>, etc. in structured content. Strip HTML in Python assertions before measuring:

import re
clean_text = re.sub(r'<[^>]+>', '', raw_text)

Echo Provider (Preview Mode)