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Getting started

After installing promptfoo, you can set up your first config file in two ways:

Running with Example​

Set up your first config file with a pre-built example by running this command with npx, npm, or brew:

npx promptfoo@latest init --example getting-started

This will create a new directory with a basic example that tests translation prompts across different models. The example includes:

  • A configuration file promptfooconfig.yaml with sample prompts, providers, and test cases.
  • A README.md file explaining how the example works.

Starting from Scratch​

If you prefer to start from scratch instead of using the example, simply run promptfoo init without the --example flag:

npx promptfoo@latest init

The command will guide you through an interactive setup process to create your custom configuration.

Configuration​

Next, we can review the example configuration file and make changes to it.

  1. Set up your prompts: Open promptfooconfig.yaml and add prompts that you want to test. Use double curly braces as placeholders for variables: {{variable_name}}. For example:

    prompts:
      - 'Convert this English to {{language}}: {{input}}'
      - 'Translate to {{language}}: {{input}}'

    » More information on setting up prompts

  2. Add providers to specify which AI models you want to test. promptfoo supports 50+ providers including OpenAI, Anthropic, Google, and many others:

    providers:
      - openai:gpt-4.1
      - openai:o4-mini
      - anthropic:messages:claude-sonnet-4-20250514
      - vertex:gemini-2.5-pro-exp-03-25
      # Or use your own custom provider
      - file://path/to/custom/provider.py

    Each provider is specified using a simple format: provider_name:model_name. For example:

    • openai:gpt-4.1 for GPT-4.1
    • openai:o4-mini for OpenAI's o4-mini
    • anthropic:messages:claude-sonnet-4-20250514 for Anthropic's Claude
    • bedrock:us.meta.llama3-3-70b-instruct-v1:0 for Meta's Llama 3.3 70B via AWS Bedrock

    Most providers need authentication. For example, with OpenAI:

    export OPENAI_API_KEY=sk-abc123

    You can use:

    » See our full providers documentation for detailed setup instructions for each provider.

  3. Add test inputs: Add some example inputs for your prompts. Optionally, add assertions to set output requirements that are checked automatically.

    For example:

    tests:
      - vars:
          language: French
          input: Hello world
      - vars:
          language: Spanish
          input: Where is the library?

    When writing test cases, think of core use cases and potential failures that you want to make sure your prompts handle correctly.

    » More information on setting up tests

  4. Run the evaluation: Make sure you're in the directory containing promptfooconfig.yaml, then run:

    npx promptfoo@latest eval

    This tests every prompt, model, and test case.

  5. After the evaluation is complete, open the web viewer to review the outputs:

    npx promptfoo@latest view

Promptfoo Web UI showing evaluation results

Configuration​

The YAML configuration format runs each prompt through a series of example inputs (aka "test case") and checks if they meet requirements (aka "assert").

Asserts are optional. Many people get value out of reviewing outputs manually, and the web UI helps facilitate this.

tip

See the Configuration docs for a detailed guide.

Show example YAML
promptfooconfig.yaml
# yaml-language-server: $schema=https://promptfoo.dev/config-schema.json
description: Automatic response evaluation using LLM rubric scoring

# Load prompts
prompts:
  - file://prompts.txt
providers:
  - openai:gpt-4.1
defaultTest:
  assert:
    - type: llm-rubric
      value: Do not mention that you are an AI or chat assistant
    - type: javascript
      # Shorter is better
      value: Math.max(0, Math.min(1, 1 - (output.length - 100) / 900));
tests:
  - vars:
      name: Bob
      question: Can you help me find a specific product on your website?
  - vars:
      name: Jane
      question: Do you have any promotions or discounts currently available?
  - vars:
      name: Ben
      question: Can you check the availability of a product at a specific store location?
  - vars:
      name: Dave
      question: What are your shipping and return policies?
  - vars:
      name: Jim
      question: Can you provide more information about the product specifications or features?
  - vars:
      name: Alice
      question: Can you recommend products that are similar to what I've been looking at?
  - vars:
      name: Sophie
      question: Do you have any recommendations for products that are currently popular or trending?
  - vars:
      name: Jessie
      question: How can I track my order after it has been shipped?
  - vars:
      name: Kim
      question: What payment methods do you accept?
  - vars:
      name: Emily
      question: Can you help me with a problem I'm having with my account or order?

Examples​

Prompt quality​

In this example, we evaluate whether adding adjectives to the personality of an assistant bot affects the responses.

You can quickly set up this example by running:

npx promptfoo@latest init --example self-grading

Here is the configuration:

promptfooconfig.yaml
# yaml-language-server: $schema=https://promptfoo.dev/config-schema.json
# Load prompts
prompts:
  - file://prompts.txt

# Set an LLM
providers:
  - openai:gpt-4.1

# These test properties are applied to every test
defaultTest:
  assert:
    # Ensure the assistant doesn't mention being an AI
    - type: llm-rubric
      value: Do not mention that you are an AI or chat assistant

    # Prefer shorter outputs using a scoring function
    - type: javascript
      value: Math.max(0, Math.min(1, 1 - (output.length - 100) / 900));

# Set up individual test cases
tests:
  - vars:
      name: Bob
      question: Can you help me find a specific product on your website?
  - vars:
      name: Jane
      question: Do you have any promotions or discounts currently available?
  - vars:
      name: Ben
      question: Can you check the availability of a product at a specific store location?
  - vars:
      name: Dave
      question: What are your shipping and return policies?
  - vars:
      name: Jim
      question: Can you provide more information about the product specifications or features?
  - vars:
      name: Alice
      question: Can you recommend products that are similar to what I've been looking at?
  - vars:
      name: Sophie
      question: Do you have any recommendations for products that are currently popular or trending?
  - vars:
      name: Jessie
      question: How can I track my order after it has been shipped?
  - vars:
      name: Kim
      question: What payment methods do you accept?
  - vars:
      name: Emily
      question: Can you help me with a problem I'm having with my account or order?

A simple npx promptfoo@latest eval will run this example from the command line:

promptfoo command line

This command will evaluate the prompts, substituting variable values, and output the results in your terminal.

Have a look at the setup and full output here.

You can also output a nice spreadsheet, JSON, YAML, or an HTML file:

npx promptfoo@latest eval -o output.html

Table output

Model quality​

In this next example, we evaluate the difference between GPT-4.1 and o4-mini outputs for a given prompt:

You can quickly set up this example by running:

npx promptfoo@latest init --example gpt-4o-vs-4o-mini
promptfooconfig.yaml
prompts:
  - file://prompt1.txt
  - file://prompt2.txt

# Set the LLMs we want to test
providers:
  - openai:o4-mini
  - openai:gpt-4.1

A simple npx promptfoo@latest eval will run the example. Also note that you can override parameters directly from the command line. For example, this command:

npx promptfoo@latest eval -p prompts.txt -r openai:o4-mini openai:gpt-4.1 -o output.html

Produces this HTML table:

Side-by-side evaluation of LLM model quality, gpt-4.1 vs o4-mini, html output

Full setup and output here.

A similar approach can be used to run other model comparisons. For example, you can:

Other examples​

There are many examples available in the examples/ directory of our Github repository.

Automatically assess outputs​

The above examples create a table of outputs that can be manually reviewed. By setting up assertions, you can automatically grade outputs on a pass/fail basis.

For more information on automatically assessing outputs, see Assertions & Metrics.