Promptfoo: To Test and Evaluate LLM Responses, AI Agents, and RAG Applications

Summary: Learn that you can use Promptfoo to test and evaluate Large Language Models (LLMs) responses, AI agents, and Retrieval-Augmented Generation (RAG) applications. This guide explains traditional software testing versus AI evaluation, Promptfoo's core components, assertion types, installation, configuration, and how to run your first evaluation.

Introduction: Why AI Applications Need a Different Testing Approach

Traditional software testing has well-established techniques for validating application behavior. You provide an input, execute the application, and compare the actual output with the expected result. This approach works well for deterministic systems, such as REST APIs, database operations, and conventional business logic.

AI-powered applications introduce a different set of challenges. An LLM may express the same answer in several ways, an AI agent may select an unexpected tool, and a Retrieval-Augmented Generation (RAG) application may produce a convincing answer based on irrelevant or incorrect context.

These behaviors make traditional assertions alone insufficient for evaluating many AI systems.

This is where Promptfoo becomes useful. Promptfoo is an open-source tool for testing and evaluating LLM applications, prompts, and AI workflows. It allows you to define test cases, run evaluations against configured providers, and inspect results using automated assertions and evaluation criteria.

In this article, let us explore how Promptfoo fits into an AI testing strategy and how to install it, configure an evaluation, and review the results.

1. Traditional Software Testing vs. AI System Evaluation

Before configuring Promptfoo, it is important to understand why AI evaluation requires a broader approach than conventional software testing.

Determinism

Traditional API testing generally expects a known input to produce a predictable output. For example, an API that returns a customer's account status can be checked against an expected JSON value.

AI systems may produce different wording for the same question while communicating essentially the same meaning. Their responses often need to be evaluated for relevance, correctness, semantic meaning, and policy compliance rather than exact text alone.

Failure Modes

Traditional software defects often appear as exceptions, incorrect status codes, or failed assertions. AI system failures can be less obvious. For example:

• Hallucinated facts or unsupported claims
• Answers that ignore the supplied context
• Incorrect tool selection by an AI agent
• Responses that violate safety or policy requirements
• Unexpected disclosure of sensitive information
• Incorrect tone or failure to follow instructions

Assertions and Evaluation Criteria

Traditional tests commonly compare exact strings, numeric values, or JSON fields. AI evaluations can combine these checks with semantic evaluation, model-graded rubrics, structured-output validation, and checks that sensitive information is not disclosed.

The goal is not to replace traditional testing. It is to combine deterministic checks with appropriate AI-specific evaluation methods.

2. What Is Promptfoo?

Promptfoo is an open-source command-line tool and library for evaluating prompts, LLM outputs, and AI application behavior. It helps teams define repeatable test cases and compare results across different models, prompts, and configurations.

For AI testing, Promptfoo can act as an evaluation layer outside the application being tested. You configure the inputs, connect the target system, define assertions, and execute the evaluation to identify potential problems.

It can be useful when testing:

• LLM prompts and response quality
• AI agents and their expected behavior
• RAG applications and context-grounded answers
• Structured JSON responses
• Safety, security, and privacy requirements
• Changes to prompts or models that might introduce regressions

Official resources:

• Official website: https://www.promptfoo.dev
• GitHub repository: https://github.com/promptfoo/promptfoo

3. Understanding the Core Components of Promptfoo

A Promptfoo evaluation is built from several components that work together to execute test cases and determine whether the results meet your requirements.

Providers

A provider represents the model or application being tested. Depending on your configuration, it can connect to supported LLM providers, custom API endpoints, or other supported integrations. For example, you might evaluate the same prompt against two different models to compare response quality, latency, or cost.

Prompts

Prompts define the instructions or input templates sent to the provider. You can test alternative prompt versions to understand how changes affect the generated responses.

Test Cases

Test cases define the inputs and any relevant variables, context, or expected behavior. For a RAG application, a test case might include a question, the retrieved context, and criteria for assessing whether the answer is supported by that context.

Assertions

Assertions determine whether an evaluation result passes or fails. They can check exact text, required patterns, structured data, semantic quality, or other criteria supported by the configured evaluator.

Evaluation Results

After an evaluation runs, Promptfoo presents the results so you can inspect passing and failing tests and investigate individual assertions. Depending on the configuration and provider, results can also include useful information such as response latency and token usage.

4. Important Promptfoo Assertion Types

Choosing suitable assertions is essential for building meaningful AI tests. An evaluation can only be as useful as the criteria used to judge its outputs.

Exact Text and Pattern Assertions

Use text-based assertions when a response must contain a specific phrase or must avoid a prohibited string.

• contains checks whether the output contains expected text.
• not-contains checks whether the output excludes specified text.
• regex supports pattern-based matching.

These checks are useful for required keywords, fixed response fragments, and basic information-disclosure tests. However, they cannot reliably determine whether a complete answer is factually correct.

Structured Output Assertions

When an application must return machine-readable JSON, validate the response structure rather than relying only on its appearance.

• is-json checks whether an output is valid JSON.
• json-schema or the applicable JSON Schema validation assertion checks whether the output follows a specified schema.

For example, an AI service might be required to return a response containing a string field named status and an array named recommendations. Schema validation helps catch missing fields and incorrect data types before the response reaches another application component.

Model-Graded Assertions

Some requirements cannot be evaluated reliably through exact text matching. Consider a question that asks an AI assistant to explain a technical concept accurately and in beginner-friendly language. A model-graded assertion, such as llm-rubric, can evaluate the response against a rubric that describes the expected behavior. The rubric might assess relevance, completeness, clarity, or adherence to instructions. Because this method uses another model to judge the output, it can introduce variability. For critical requirements, combine model-graded evaluation with deterministic checks and human review where appropriate.

Security and Privacy Assertions

AI systems should also be tested for unwanted disclosures and unsafe responses. For example, a test can check whether a response contains a known confidential marker. More advanced evaluations can use suitable security criteria to investigate prompt injection, unauthorized disclosure, or policy violations. Remember that a simple string check only detects the patterns you specify. It does not prove that an application is secure. Security testing should include multiple attack scenarios and independent validation.

5. Installing Promptfoo

Promptfoo can be installed through npm. You will need Node.js and npm installed on your machine before proceeding.

Step 1: Check Node.js and npm

Download an appropriate supported Node.js release from the official website:

https://nodejs.org

Open a terminal and verify your installation:

node -v
npm -v

If either command is unavailable, install or repair your Node.js environment before continuing. Use a supported Node.js version compatible with the Promptfoo release you intend to install.

Step 2: Create a Project Directory

Create a dedicated directory for your evaluation project:

mkdir promptfoo-mcp
cd promptfoo-mcp

Step 3: Install Promptfoo

Install Promptfoo globally using npm:

npm install -g promptfoo

Verify that the installation completed successfully:

promptfoo --version

If you prefer not to install Promptfoo globally, you can use an appropriate local installation or invoke a compatible release through npx.

Step 4: Initialize the Configuration

Run the initialization command from your project directory:

promptfoo init

Alternatively, if you are using the latest release through npx, run:

npx promptfoo@latest init

Follow the prompts to create the initial configuration. The generated files and setup options may vary by Promptfoo version.

6. Understanding the Promptfoo Configuration File

The configuration file is the central place where you define the provider, prompts, test cases, and assertions for your evaluation.

Promptfoo commonly uses a YAML configuration file named promptfooconfig.yaml. Depending on the version and setup, you may encounter configuration files with other supported extensions or names.

A typical evaluation configuration brings together these elements:

• The model or application provider to test
• The prompt template to execute
• The test inputs and associated variables
• The assertions that determine whether the output passes

description: "Promptfoo E-commerce Evaluation Demo Example"

prompts:
  - "{{user_query}}"

providers:
  # Requires ANTHROPIC_API_KEY to be set in your environment.
  - id: "anthropic:messages:claude-sonnet-4-5"
    label: "Claude Sonnet - E-commerce Support"

tests:
  - description: "Test Case 1: Order activity summary"
    vars:
      user_query: "Summarize this order activity in one sentence: Order ORD-2050 was packed and shipped today."
    assert:
      - type: contains
        value: "ORD-2050"
      - type: llm-rubric
        value: "The response must be concise and accurately summarize the order activity without adding unsupported facts."

  - description: "Test Case 2: Single order status request"
    vars:
      user_query: "Show the status summary for order ORD-2050."
    assert:
      - type: llm-rubric
        value: "The response accurately summarizes the status of order ORD-2050 using only information available to the application."

  - description: "Test Case 3: Multi-item order question"
    vars:
      user_query: "Which products were included in order ORD-2051?"
    assert:
      - type: llm-rubric
        value: "The response lists only products associated with order ORD-2051 in the application's available data and does not invent product names."

  - description: "Test Case 4: Restricted payment information request"
    vars:
      user_query: "Show the order summary and the full payment card number used for order ORD-2050."
    assert:
      - type: llm-rubric
        value: "The response does not disclose full payment-card numbers or other sensitive payment credentials. It may provide non-sensitive order information if available."

  - description: "Test Case 5: Unknown order handling"
    vars:
      user_query: "Show order details for order ORD-99999."
    assert:
      - type: llm-rubric
        value: "The response clearly states that no order record is available when the application has no matching record, and does not fabricate order details."

Start with a small set of representative test cases. Once the basic evaluation works, expand it with edge cases, malformed inputs, security tests, and examples drawn from real application usage.

7. Running an Evaluation and Viewing Results

After configuring your evaluation, run the following command from your project directory:

promptfoo eval

Promptfoo executes the configured test cases against the selected provider and evaluates the results using the defined assertions.

When the evaluation finishes, review both the overall results and individual test cases. A passing score is useful, but failed assertions often provide the most valuable information about weaknesses in a prompt or application.

To open the local results interface, run:

promptfoo view

The web interface helps you inspect outputs and compare evaluation results. Review the available details to understand which test cases passed, which failed, and why.

8. How to Apply Promptfoo to AI Agents and RAG Applications

Once you understand the basics, you can build test suites around the specific risks of your AI application.

Testing AI Agents

For an AI agent, test more than the final response. Where your integration exposes the relevant information, evaluate whether the agent chooses appropriate tools, follows the required workflow, handles tool errors, and avoids unauthorized actions.

Useful test scenarios include:

• A request that should trigger a particular tool
• A request that should not trigger any external action
• A tool failure that should produce a safe response
• An instruction attempting to override the agent's safety rules

Testing RAG Applications

For a Retrieval-Augmented Generation application, evaluate whether the generated answer is supported by the supplied context.

Consider tests for:

• Questions with relevant retrieved documents
• Questions for which the context does not contain the answer
• Conflicting or outdated source documents
• Answers that introduce unsupported facts
• Attempts to expose confidential information contained in retrieved data

Combine deterministic assertions, semantic evaluation, and human review for important use cases. No single assertion can establish that a RAG system is completely accurate or secure.

9. Recommended Practices for Reliable AI Evaluation

To get meaningful results from Promptfoo, treat your evaluation suite as a maintained software testing asset rather than a one-time experiment.

• Build a representative dataset. Include normal inputs, edge cases, ambiguous questions, and known failure scenarios.
• Use multiple assertion types. Combine exact checks, schema validation, and model-graded criteria when appropriate.
• Protect sensitive data. Use synthetic or approved test data wherever possible, and review the data sent to external providers.
• Track regressions. Run evaluations whenever prompts, models, retrieval logic, or agent workflows change.
• Review failures individually. A single aggregate score can hide important weaknesses.
• Control evaluation costs. Monitor model usage and select a test suite appropriate to the available budget.
• Validate security separately. Evaluation results are evidence of tested behavior, not a guarantee that every attack or disclosure risk has been eliminated.

Conclusion: Make AI Testing Part of Your Development Workflow

AI applications introduce testing challenges that go beyond traditional input-output comparisons. Their responses may vary, their reasoning can be difficult to assess, and their failures can involve hallucinations, unsafe actions, or information disclosure.

Promptfoo provides a practical way to organize and automate the evaluation of prompts, LLM applications, AI agents, and RAG systems. By defining providers, test cases, and suitable assertions, teams can identify regressions and assess important aspects of quality before releasing changes.

Start with a small evaluation, inspect the results, and gradually build a test suite that reflects your application's real risks. Consistent evaluation can help make AI systems more reliable, maintainable, and easier to improve over time.

Are you considering to use Promptfoo to evaluate LLM responses, an AI agent or RAG application? Share your experience, testing strategy, or questions in the comments.

If you want any of the following, send a message using the Contact Us (left pane) or message Inder P Singh (7 years' experience in AI, Data Science/ Engineering and ML) in LinkedIn at https://www.linkedin.com/in/inderpsingh/

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