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Configuration Overview - Getting Started with Promptfoo

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Configuration

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

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

Example

Let's imagine we're building an app that does language translation. This config runs each prompt through GPT-4.1 and Gemini, substituting language and input variables:

yaml
prompts:
  - file://prompt1.txt
  - file://prompt2.txt
providers:
  - openai:gpt-5-mini
  - vertex:gemini-2.0-flash-exp
tests:
  - vars:
      language: French
      input: Hello world
  - vars:
      language: German
      input: How's it going?

:::tip

For more information on setting up a prompt file, see input and output files.

:::

Running promptfoo eval over this config will result in a matrix view that you can use to evaluate GPT vs Gemini.

Use assertions to validate output

Next, let's add an assertion. This automatically rejects any outputs that don't contain JSON:

yaml
prompts:
  - file://prompt1.txt
  - file://prompt2.txt
providers:
  - openai:gpt-5-mini
  - vertex:gemini-2.0-flash-exp
tests:
  - vars:
      language: French
      input: Hello world
    // highlight-start
    assert:
      - type: contains-json
    // highlight-end
  - vars:
      language: German
      input: How's it going?

We can create additional tests. Let's add a couple other types of assertions. Use an array of assertions for a single test case to ensure all conditions are met.

In this example, the javascript assertion runs Javascript against the LLM output. The similar assertion checks for semantic similarity using embeddings:

yaml
prompts:
  - file://prompt1.txt
  - file://prompt2.txt
providers:
  - openai:gpt-5-mini
  - vertex:gemini-2.0-flash-exp
tests:
  - vars:
      language: French
      input: Hello world
    assert:
      - type: contains-json
      // highlight-start
      - type: javascript
        value: output.toLowerCase().includes('bonjour')
      // highlight-end
  - vars:
      language: German
      input: How's it going?
    assert:
      // highlight-start
      - type: similar
        value: was geht
        threshold: 0.6   # cosine similarity
      // highlight-end

:::tip To learn more about assertions, see docs on configuring assertions and metrics. :::

Import providers from separate files

The providers config property can point to a list of files. For example:

yaml
providers:
  - file://path/to/provider1.yaml
  - file://path/to/provider2.json

Where the provider file looks like this:

yaml
id: openai:gpt-5-mini
label: Foo bar
config:
  temperature: 0.9

Import tests from separate files

The tests config property takes a list of paths to files or directories. For example:

yaml
prompts: file://prompts.txt
providers: openai:gpt-5-mini

# Load & runs all test cases matching these filepaths
tests:
  # You can supply an exact filepath
  - file://tests/tests2.yaml

  # Or a glob (wildcard)
  - file://tests/*

  # Mix and match with actual test cases
  - vars:
      var1: foo
      var2: bar

A single string is also valid:

yaml
tests: file://tests/*

Or a list of paths:

yaml
tests:
  - file://tests/accuracy
  - file://tests/creativity
  - file://tests/hallucination

:::tip Test files can be defined in YAML/JSON, JSONL, CSV, and TypeScript/JavaScript. We also support Google Sheets CSV datasets. :::

Import vars from separate files

The vars property can point to a file or directory. For example:

yaml
tests:
  - vars: file://path/to/vars*.yaml

You can also load individual variables from file by using the file:// prefix. For example:

yaml
tests:
  - vars:
      var1: some value...
      var2: another value...
      var3: file://path/to/var3.txt

Javascript and Python variable files are supported. For example:

yaml
tests:
  - vars:
      context: file://fetch_from_vector_database.py

Scripted vars are useful when testing vector databases like Pinecone, Chroma, Milvus, etc. You can communicate directly with the database to fetch the context you need.

PDFs are also supported and can be used to extract text from a document:

yaml
tests:
  - vars:
      paper: file://pdfs/arxiv_1.pdf

Note that you must install the pdf-parse package to use PDFs as variables:

npm install pdf-parse

Javascript variables

To dynamically load a variable from a JavaScript file, use the file:// prefix in your YAML configuration, pointing to a JavaScript file that exports a function.

yaml
tests:
  - vars:
      context: file://path/to/dynamicVarGenerator.js

The function receives varName, prompt, otherVars, and provider as arguments:

js
module.exports = async function (varName, prompt, otherVars, provider) {
  // Access other variables from the test case
  const role = otherVars.role;

  // Return the dynamic value
  return { output: PROMPTS[role] };

  // Or return an error
  // return { error: 'Something went wrong' };
};

See the dynamic-var example for a complete working example.

Python variables

Define a get_var function that accepts var_name, prompt, and other_vars:

yaml
tests:
  - vars:
      context: file://load_context.py
python
def get_var(var_name, prompt, other_vars):
    # Access other variables from the test case
    role = other_vars.get("role")

    # Return the dynamic value
    return {"output": PROMPTS[role]}

    # Or return an error
    # return {"error": "Something went wrong"}

Avoiding repetition

Default test cases

Use defaultTest to set properties for all tests.

In this example, we use a llm-rubric assertion to ensure that the LLM does not refer to itself as an AI. This check applies to all test cases:

yaml
prompts:
  - file://prompt1.txt
  - file://prompt2.txt
providers:
  - openai:gpt-5-mini
  - vertex:gemini-2.0-flash-exp
// highlight-start
defaultTest:
  assert:
    - type: llm-rubric
      value: does not describe self as an AI, model, or chatbot
// highlight-end
tests:
  - vars:
      language: French
      input: Hello world
    assert:
      - type: contains-json
      - type: javascript
        value: output.toLowerCase().includes('bonjour')
  - vars:
      language: German
      input: How's it going?
    assert:
      - type: similar
        value: was geht
        threshold: 0.6

You can also use defaultTest to override the model used for each test. This can be useful for model-graded evals:

yaml
defaultTest:
  options:
    provider: openai:gpt-5-mini-0613

Set options.disableDefaultAsserts: true on a test case when that test should define its own assertions without inheriting defaultTest.assert. Other defaultTest fields, such as vars, metadata, threshold, and options, still apply:

yaml
defaultTest:
  vars:
    audience: developer
  assert:
    - type: contains
      value: installation steps

tests:
  - vars:
      topic: API setup
    options:
      disableDefaultAsserts: true
    assert:
      - type: contains-json

Default variables

Use defaultTest to define variables that are shared across all tests:

yaml
defaultTest:
  vars:
    template: 'A reusable prompt template with {{shared_var}}'
    shared_var: 'some shared content'

tests:
  - vars:
      unique_var: value1
  - vars:
      unique_var: value2
      shared_var: 'override shared content' # Optionally override defaults

Loading defaultTest from external files

You can load defaultTest configuration from external files using defaultTest: file://path/to/config.yaml for sharing test configurations across projects.

YAML references

promptfoo configurations support JSON schema references, which define reusable blocks.

Use the $ref key to re-use assertions without having to fully define them more than once. Here's an example:

yaml
prompts:
  - file://prompt1.txt
  - file://prompt2.txt
providers:
  - openai:gpt-5-mini
  - vertex:gemini-2.0-flash-exp
tests:
  - vars:
      language: French
      input: Hello world
    assert:
      - $ref: '#/assertionTemplates/startsUpperCase'
  - vars:
      language: German
      input: How's it going?
    assert:
      - $ref: '#/assertionTemplates/noAIreference'
      - $ref: '#/assertionTemplates/startsUpperCase'

// highlight-start
assertionTemplates:
    noAIreference:
      type: llm-rubric
      value: does not describe self as an AI, model, or chatbot
    startsUpperCase:
      type: javascript
      value: output[0] === output[0].toUpperCase()
// highlight-end

:::info tools and functions values in providers config are not dereferenced. This is because they are standalone JSON schemas that may contain their own internal references. :::

Multiple variables in a single test case

The vars map in the test also supports array values. If values are an array, the test case will run each combination of values.

For example:

yaml
prompts: file://prompts.txt
providers:
  - openai:gpt-5-mini
  - openai:gpt-5
tests:
  - vars:
      // highlight-start
      language:
        - French
        - German
        - Spanish
      input:
        - 'Hello world'
        - 'Good morning'
        - 'How are you?'
      // highlight-end
    assert:
      - type: similar
        value: 'Hello world'
        threshold: 0.8

Evaluates each language x input combination:

Vars can also be imported from globbed filepaths. They are automatically expanded into an array. For example:

yaml
  - vars:
      language:
        - French
        - German
        - Spanish
      // highlight-start
      input: file://path/to/inputs/*.txt
      // highlight-end

Using nunjucks templates

Use Nunjucks templates to exert additional control over your prompt templates, including loops, conditionals, and more.

Manipulating objects

In the above examples, vars values are strings. But vars can be any JSON or YAML entity, including nested objects. You can manipulate these objects in the prompt, which are nunjucks templates:

promptfooconfig.yaml:

yaml
tests:
  - vars:
      user_profile:
        name: John Doe
        interests:
          - reading
          - gaming
          - hiking
      recent_activity:
        type: reading
        details:
          title: 'The Great Gatsby'
          author: 'F. Scott Fitzgerald'

prompt.txt:

liquid
User Profile:
- Name: {{ user_profile.name }}
- Interests: {{ user_profile.interests | join(', ') }}
- Recent Activity: {{ recent_activity.type }} on "{{ recent_activity.details.title }}" by {{ recent_activity.details.author }}

Based on the above user profile, generate a personalized reading recommendation list that includes books similar to "{{ recent_activity.details.title }}" and aligns with the user's interests.

Here's another example. Consider this test case, which lists a handful of user and assistant messages in an OpenAI-compatible format:

yaml
tests:
  - vars:
      previous_messages:
        - role: user
          content: hello world
        - role: assistant
          content: how are you?
        - role: user
          content: great, thanks

The corresponding prompt.txt file simply passes through the previous_messages object using the dump filter to convert the object to a JSON string:

nunjucks
{{ previous_messages | dump }}

Running promptfoo eval -p prompt.txt -c path_to.yaml will call the Chat Completion API with the following prompt:

json
[
  {
    "role": "user",
    "content": "hello world"
  },
  {
    "role": "assistant",
    "content": "how are you?"
  },
  {
    "role": "user",
    "content": "great, thanks"
  }
]

Escaping JSON strings

If the prompt is valid JSON, nunjucks variables are automatically escaped when they are included in strings:

yaml
tests:
  - vars:
      system_message: >
        This multiline "system message" with quotes...
        Is automatically escaped in JSON prompts!
json
{
  "role": "system",
  "content": "{{ system_message }}"
}

You can also manually escape the string using the nunjucks dump filter. This is necessary if your prompt is not valid JSON, for example if you are using nunjucks syntax:

liquid
{
  "role": {% if 'admin' in message %} "system" {% else %} "user" {% endif %},
  "content": {{ message | dump }}
}

Variable composition

Variables can reference other variables:

yaml
prompts:
  - 'Write a {{item}}'

tests:
  - vars:
      item: 'tweet about {{topic}}'
      topic: 'bananas'

  - vars:
      item: 'instagram about {{topic}}'
      topic: 'theoretical quantum physics in alternate dimensions'

Accessing environment variables

You can access environment variables in your templates using the env global:

yaml
prompts:
  - 'file://{{ env.PROMPT_DIR }}/prompt.txt'

tests:
  - vars:
      headline: 'Articles about {{ env.TOPIC }}'

Environment variables are resolved at config load time (not runtime) and can control file paths and API keys—only use them in trusted environments.

:::warning

Avoid copying secrets into config.env with templates like ANTHROPIC_API_KEY: '{{ env.ANTHROPIC_API_KEY }}'. This resolves the secret into the eval config object and may appear in exported results.

If a secret is already present in your shell environment (or loaded via --env-file), prefer reading it directly from process env and keep config.env for non-sensitive flags.

:::

Tools and Functions

promptfoo supports tool use and function calling with Google, OpenAI and Anthropic models, as well as other provider-specific configurations like temperature and number of tokens. For more information on defining functions and tools, see the Google Vertex provider docs, Google AIStudio provider docs, Google Live provider docs, OpenAI provider docs and the Anthropic provider docs.

Thinking Output

Some models, like Anthropic's Claude and DeepSeek, support thinking/reasoning capabilities that allow the model to show its reasoning process before providing a final answer.

This is useful for reasoning tasks or understanding how the model arrived at its conclusion.

Controlling Thinking Output

By default, thinking content is included in the response. You can hide it by setting showThinking to false.

For example, for Claude:

yaml
providers:
  - id: anthropic:messages:claude-sonnet-4-5-20250929
    config:
      thinking:
        type: 'enabled'
        budget_tokens: 16000
      showThinking: false # Exclude thinking content from output

This is useful when you want better reasoning but don't want to expose the thinking process to your assertions.

For more details on extended thinking capabilities, see the Anthropic provider docs and AWS Bedrock provider docs.

Transforming outputs

Transforms can be applied at multiple levels in the evaluation pipeline:

Transform execution order

  1. Provider transforms (transformResponse) - Always applied first
  2. Test transforms (options.transform) and Context transforms (contextTransform)
    • Both receive the output from the provider transform
    • Test transforms modify the output for assertions
    • Context transforms extract context for context-based assertions (e.g., context-faithfulness)

Test transform hierarchy

For test transforms specifically:

  1. Default test transforms (if specified in defaultTest)
  2. Individual test case transforms (overrides defaultTest transform if present)

Note that only one transform is applied at the test case level - either from defaultTest or the individual test case, not both.

The TestCase.options.transform field is a Javascript snippet that modifies the LLM output before it is run through the test assertions.

It is a function that takes a string output and a context object:

typescript
transformFn: (output: string, context: {
  prompt: {
    // ID of the prompt, if assigned
    id?: string;
    // Raw prompt as provided in the test case, without {{variable}} substitution.
    raw?: string;
    // Prompt as sent to the LLM API and assertions.
    display?: string;
  };
  vars?: Record<string, any>;
  // Metadata returned in the provider response.
  metadata?: Record<string, any>;
}) => void;

This is useful if you need to somehow transform or clean LLM output before running an eval.

For example:

yaml
# ...
tests:
  - vars:
      language: French
      body: Hello world
    options:
      // highlight-start
      transform: output.toUpperCase()
      // highlight-end
    # ...

Or multiline:

yaml
# ...
tests:
  - vars:
      language: French
      body: Hello world
    options:
      // highlight-start
      transform: |
        output = output.replace(context.vars.language, 'foo');
        const words = output.split(' ').filter(x => !!x);
        return JSON.stringify(words);
      // highlight-end
    # ...

It also works in assertions, which is useful for picking values out of JSON:

yaml
tests:
  - vars:
      # ...
    assert:
      - type: equals
        value: 'foo'
        transform: output.category # Select the 'category' key from output json

:::tip Use defaultTest apply a transform option to every test case in your test suite. :::

:::tip When using the Node.js package, you can pass functions directly as transform, transformVars, and contextTransform values instead of string expressions. :::

Transforms from separate files

Transform functions can be executed from external JavaScript or Python files. You can optionally specify a function name to use.

For JavaScript:

yaml
defaultTest:
  options:
    transform: file://transform.js:customTransform
js
module.exports = {
  customTransform: (output, context) => {
    // context.vars, context.prompt
    return output.toUpperCase();
  },
};

For Python:

yaml
defaultTest:
  options:
    transform: file://transform.py
python
def get_transform(output, context):
    # context['vars'], context['prompt']
    return output.upper()

If no function name is specified for Python files, it defaults to get_transform. To use a custom Python function, specify it in the file path:

yaml
transform: file://transform.py:custom_python_transform

Transforming input variables

You can also transform input variables before they are used in prompts using the transformVars option. This feature is useful when you need to pre-process data or load content from external sources.

The transformVars function should return an object with the transformed variable names and values. These transformed variables are added to the vars object and can override existing keys. For example:

yaml
prompts:
  - 'Summarize the following text in {{topic_length}} words: {{processed_content}}'

defaultTest:
  options:
    transformVars: |
      return {
        uppercase_topic: vars.topic.toUpperCase(),
        topic_length: vars.topic.length,
        processed_content: vars.content.trim()
      };

tests:
  - vars:
      topic: 'climate change'
      content: '  This is some text about climate change that needs processing.  '
    assert:
      - type: contains
        value: '{{uppercase_topic}}'

Transform functions can also be specified within individual test cases.

yaml
tests:
  - vars:
      url: 'https://example.com/image.png'
    options:
      transformVars: |
        return { ...vars, image_markdown: `![image](${vars.url})` }

Input transforms from separate files

For more complex transformations, you can use external files for transformVars:

yaml
defaultTest:
  options:
    transformVars: file://transformVars.js:customTransformVars
js
const fs = require('fs');

module.exports = {
  customTransformVars: (vars, context) => {
    try {
      return {
        uppercase_topic: vars.topic.toUpperCase(),
        topic_length: vars.topic.length,
        file_content: fs.readFileSync(vars.file_path, 'utf-8'),
      };
    } catch (error) {
      console.error('Error in transformVars:', error);
      return {
        error: 'Failed to transform variables',
      };
    }
  },
};

You can also define transforms in python.

yaml
defaultTest:
  options:
    transformVars: file://transform_vars.py
python
import os

def get_transform(vars, context):
    with open(vars['file_path'], 'r') as file:
        file_content = file.read()

    return {
        'uppercase_topic': vars['topic'].upper(),
        'topic_length': len(vars['topic']),
        'file_content': file_content,
        'word_count': len(file_content.split())
    }

Config structure and organization

For detailed information on the config structure, see Configuration Reference.

If you have multiple sets of tests, it helps to split them into multiple config files. Use the --config or -c parameter to run each individual config:

promptfoo eval -c usecase1.yaml

and

promptfoo eval -c usecase2.yaml

You can run multiple configs at the same time, which will combine them into a single eval. For example:

promptfoo eval -c my_configs/*

or

promptfoo eval -c config1.yaml -c config2.yaml -c config3.yaml

Loading tests from CSV

YAML is nice, but some organizations maintain their LLM tests in spreadsheets for ease of collaboration. promptfoo supports a special CSV file format.

yaml
prompts:
  - file://prompt1.txt
  - file://prompt2.txt
providers:
  - openai:gpt-5-mini
  - vertex:gemini-2.0-flash-exp
// highlight-next-line
tests: file://tests.csv

promptfoo also has built-in ability to pull test cases from a Google Sheet. The easiest way to get started is to set the sheet visible to "anyone with the link". For example:

yaml
prompts:
  - file://prompt1.txt
  - file://prompt2.txt
providers:
  - openai:gpt-5-mini
  - vertex:gemini-2.0-flash-exp
// highlight-next-line
tests: https://docs.google.com/spreadsheets/d/1eqFnv1vzkPvS7zG-mYsqNDwOzvSaiIAsKB3zKg9H18c/edit?usp=sharing

Here's a full example.

See Google Sheets integration for details on how to set up promptfoo to access a private spreadsheet.