promptfoo-evaluation

# Promptfoo Evaluation ## Overview This skill provides guidance for configuring and running LLM evaluations using [Promptfoo](https://www.promptfoo.dev/), an open-source CLI tool for testing and comparing LLM outputs. ## Quick Start ```bash # 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 # 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) ```markdown You are a helpful assistant. Task: {{task}} Context: {{context}} ``` ### Chat Format (chat.json) ```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: ```json [ {"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) ```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`): ```python 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) ```yaml 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: ```yaml 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 | 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 `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. ```yaml # 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 ```bash # 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: ```yaml 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:** ```bash 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: ```python import re clean_text = re.sub(r'<[^>]+>', '', raw_text) ``` ## Echo Provider (Preview Mode) Use the **echo provider** to preview rendered prompts without making API calls: ```yaml # 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 ```bash # 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: ```json [ {"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:** ```yaml 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:** ```yaml providers: - id: anthropic:messages:claude-sonnet-4-6 config: max_tokens: 8192 # Increase for long outputs defaultTest: assert: - type: python value: file://scripts/metrics.py:check_length ``` **Python assertion for text metrics:** ```python 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