promptfoo/site/docs/getting-started.md

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Getting started

To get started, run this command:

npx promptfoo@latest init {`npm install -g promptfoo promptfoo init`} {`brew install promptfoo promptfoo init`}

This will create a promptfooconfig.yaml file in your current directory.

1. Set up your prompts: Open promptfooconfig.yaml and 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 and specify the models you want to test:

   providers:
     - openai:gpt-4o-mini
     - openai:gpt-4
   

   • OpenAI: if testing with an OpenAI model, you'll need to set the OPENAI_API_KEY environment variable (see OpenAI provider docs for more info):

     export OPENAI_API_KEY=sk-abc123
     

   • Custom: See how to call your existing Javascript, Python, any other executable or API endpoint.

   • APIs: See setup instructions for Azure, Anthropic, Mistral, HuggingFace, AWS Bedrock, and more.

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: This tests every prompt, model, and test case:

   npx promptfoo@latest eval promptfoo eval promptfoo eval

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

   npx promptfoo@latest view promptfoo view promptfoo view

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

prompts:
  - file://prompts.txt
providers:
  - openai:gpt-4o-mini
tests:
  - description: First test case - automatic review
    vars:
      var1: first variable's value
      var2: another value
      var3: some other value
    assert:
      - type: equals
        value: expected LLM output goes here
      - type: function
        value: output.includes('some text')

  - description: Second test case - manual review
    # Test cases don't need assertions if you prefer to review the output yourself
    vars:
      var1: new value
      var2: another value
      var3: third value

  - description: Third test case - other types of automatic review
    vars:
      var1: yet another value
      var2: and another
      var3: dear llm, please output your response in json format
    assert:
      - type: contains-json
      - type: similar
        value: ensures that output is semantically similar to this text
      - type: llm-rubric
        value: must contain a reference to X

Examples

Prompt quality

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

Here is the configuration:

# Load prompts
prompts:
  - file://prompt1.txt
  - file://prompt2.txt

# Set an LLM
providers:
  - openai:gpt-4o-mini

# These test properties are applied to every test
defaultTest:
  assert:
    # Verify that the output doesn't contain "AI language model"
    - type: not-contains
      value: AI language model

    # Verify that the output doesn't apologize
    - type: llm-rubric
      value: must not contain an apology

    # 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?
    assert:
      - type: contains
        value: search
  - vars:
      name: Jane
      question: Do you have any promotions or discounts currently available?
    assert:
      - type: starts-with
        value: Yes
  - vars:
      name: Ben
      question: Can you check the availability of a product at a specific store location?
  # ...

A simple npx promptfoo@latest eval will run this example from the 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 promptfoo eval -o output.html promptfoo eval -o output.html

Model quality

In this next example, we evaluate the difference between GPT 3 and GPT 4 outputs for a given prompt:

prompts:
  - file://prompt1.txt
  - file://prompt2.txt

# Set the LLMs we want to test
providers:
  - openai:gpt-4o-mini
  - openai:gpt-4

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:gpt-4o-mini openai:gpt-4o -o output.html promptfoo eval -p prompts.txt -r openai:gpt-4o-mini openai:gpt-4o -o output.html promptfoo eval -p prompts.txt -r openai:gpt-4o-mini openai:gpt-4o -o output.html

Produces this HTML table:

Full setup and output here.

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

• Compare same models with different temperatures (see GPT temperature comparison)
• Compare Llama vs. GPT (see Llama vs GPT benchmark)
• Compare Retrieval-Augmented Generation (RAG) with LangChain vs. regular GPT-4 (see LangChain example)

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 Expected Outputs.