Promptfoo

Use Promptfoo when the task is specifically about Promptfoo-based evals, regression suites, or red-team scans.

Trigger boundaries

Trigger this skill for:

• promptfooconfig.yaml creation or editing
• Promptfoo CLI usage
• Promptfoo assertions and metrics
• provider/model comparisons through Promptfoo
• Promptfoo evals for RAG, agents, or API-backed systems
• Promptfoo red-team configuration and reporting
• Promptfoo MCP server setup

Do not trigger this skill for:

• generic prompt engineering with no Promptfoo requirement
• general LLM benchmarking discussions with no Promptfoo workflow
• one-off model questions that do not need a repeatable eval harness

Repo-aligned config idioms

Promptfoo examples consistently use these patterns:

• schema header:

# yaml-language-server: $schema=https://promptfoo.dev/config-schema.json

• top-level description
• prompts, providers and/or targets
• tests with vars
• optional defaultTest.assert
• file://... references for prompts, docs, agents, tools, or local files

Use Promptfoo's idioms directly instead of inventing a custom layout.

Quick decision tree

Choose the workflow that matches the request:

• Create a first config or example suite → use promptfoo init or the helper scripts in scripts/
• Check or fix a config → inspect promptfooconfig.yaml, then run promptfoo validate
• Compare prompts or providers → use promptfoo eval
• Inspect historical runs, logs, or outputs → use promptfoo list, promptfoo show, promptfoo logs, promptfoo view
• Generate adversarial tests or security scans → use promptfoo redteam ...
• Generate datasets or assertions → use promptfoo generate ...
• Expose Promptfoo to an MCP-compatible toolchain → use promptfoo mcp

Default workflow

1. Find or create promptfooconfig.yaml
2. Make the config look like Promptfoo's real examples, not an invented abstraction
3. Validate before expensive runs
4. Run a narrow eval first
5. Review failures in CLI or web view
6. Tighten prompts, assertions, or test cases
7. Re-run at broader scale

Prefer small, representative suites before big fan-out runs.

Config shapes that match Promptfoo examples

1. Basic getting-started shape

# yaml-language-server: $schema=https://promptfoo.dev/config-schema.json
description: Getting started

prompts:
  - 'Convert the following English text to {{language}}: {{input}}'

providers:
  - openai:gpt-5.2
  - openai:gpt-5-mini

tests:
  - vars:
      language: French
      input: Hello world
    assert:
      - type: contains
        value: 'Bonjour le monde'

2. Shared assertions with defaultTest

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

prompts:
  - file://prompts.txt

providers:
  - openai:chat:gpt-5.2

defaultTest:
  assert:
    - type: llm-rubric
      value: Do not mention that you are an AI or chat assistant

tests:
  - vars:
      name: Bob
      question: Can you help me find a specific product on your website?

3. RAG eval shape

# yaml-language-server: $schema=https://promptfoo.dev/config-schema.json
description: Evaluating RAG responses using multiple quality metrics

prompts:
  - |
    You are an internal corporate chatbot.
    Respond to this query: {{query}}
    Here is some context that you can use to write your response: {{context}}

providers:
  - openai:gpt-4.1-mini

tests:
  - vars:
      query: What is the max purchase that doesn't require approval?
      context: file://docs/reimbursement.md
    assert:
      - type: contains
        value: '$500'
      - type: factuality
        value: the employee's manager is responsible for approvals
      - type: answer-relevance
        threshold: 0.9
      - type: context-faithfulness
        threshold: 0.9

4. Agent eval shape

Promptfoo supports richer provider objects for agent systems:

providers:
  - id: openai:agents:my-agent
    config:
      agent: file://./agents/my-agent.ts
      tools: file://./tools/my-tools.ts
      maxTurns: 20

Use provider objects like this when evaluating agent workflows instead of flattening everything into plain prompt strings.

Assertion strategy

Use the simplest assertion that reliably captures the requirement.

Prefer, roughly in this order:

1. deterministic assertions such as contains, icontains, regex, contains-any, is-json, latency, cost
2. focused model-assisted assertions such as llm-rubric, factuality, answer-relevance, context-faithfulness, context-recall, context-relevance
3. javascript or python assertions when built-ins are insufficient
4. assert-set when you need grouped pass criteria

Guidance:

• Use deterministic checks for formatting, policy phrases, JSON shape, tool mentions, and hard constraints
• Use RAG metrics for groundedness instead of forcing one vague rubric to do everything
• Use contains-any or contains-all when wording can vary but key content must appear
• Use javascript for custom logic only when a built-in metric does not fit
• If an assertion flakes, tighten the test or use a more appropriate metric

Prompt and provider eval workflow

1. Start from reality

If Promptfoo already has a relevant example, use it as the base:

npx promptfoo@latest init --example getting-started
npx promptfoo@latest init --example eval-rag
npx promptfoo@latest init --example compare-openai-models

If the task matches one of the helper scripts in scripts/, use the script to scaffold a repo-aligned starter config quickly.

2. Validate

npx promptfoo@latest validate

If the config is not in the current directory, pass the correct path.

3. Run a narrow eval first

npx promptfoo@latest eval
npx promptfoo@latest eval --filter-first-n 5
npx promptfoo@latest eval --filter-pattern "refund|billing"
npx promptfoo@latest eval --filter-providers "openai|anthropic"
npx promptfoo@latest eval --max-concurrency 4

Use filters for large or expensive suites.

4. Review and iterate

npx promptfoo@latest view
npx promptfoo@latest list evals
npx promptfoo@latest show <eval-id>
npx promptfoo@latest logs

Focus on:

• repeated failures across one provider
• prompt-specific regressions
• flaky assertions
• cost or latency regressions
• app-path failures hidden by toy prompt-only tests

RAG and agent evaluation guidance

For RAG:

• use realistic retrieved context
• prefer file://... documents when the source material is local
• use factuality, answer-relevance, context-recall, context-relevance, and context-faithfulness where appropriate
• keep at least some deterministic assertions for hard facts

For agents:

• test tool-use behavior, not just surface prose
• include assertions for tool mentions, valid outputs, or trajectory/tool behavior if traces are available
• use provider objects and file-backed agent/tool definitions when that mirrors the real app

Red-team workflow

Use this when the goal is jailbreak testing, prompt injection resistance, policy enforcement, data exposure checks, or broader AI vulnerability scanning.

Fast path

npx promptfoo@latest redteam setup
npx promptfoo@latest redteam run
npx promptfoo@latest redteam report

No-GUI path

npx promptfoo@latest redteam init --no-gui

What to define carefully

For red-team configs, pay special attention to:

• purpose - what the system does, who the legitimate user is, and what must stay protected
• targets - HTTP endpoint, model provider, custom script, or direct app integration
• plugin selection - select the relevant attack classes instead of blindly enabling everything
• strategy selection - increase sophistication only as needed

Use stable target labels over time so reports stay comparable.

Review checklist

After a scan, summarize:

• which vulnerability classes failed
• severity and reproducibility
• exact broken policy or trust boundary
• whether mitigations belong in prompts, retrieval, app logic, tool permissions, or filters
• which failures should become normal regression tests

Do not leave red-team results as a one-off report. Convert serious failures into permanent eval coverage.

Useful CLI commands

npx promptfoo@latest init
npx promptfoo@latest init --example getting-started
npx promptfoo@latest init --example eval-rag
npx promptfoo@latest init --example compare-openai-models
npx promptfoo@latest validate
npx promptfoo@latest eval
npx promptfoo@latest eval --resume
npx promptfoo@latest eval --retry-errors
npx promptfoo@latest list evals
npx promptfoo@latest show <eval-id>
npx promptfoo@latest logs
npx promptfoo@latest view
npx promptfoo@latest generate dataset
npx promptfoo@latest generate assertions
npx promptfoo@latest redteam setup
npx promptfoo@latest redteam run
npx promptfoo@latest redteam report
npx promptfoo@latest mcp --transport stdio

Cost and speed hygiene

Promptfoo can fan out quickly. Keep it under control.

• start with a narrow provider set
• start with a small test sample
• cap concurrency when providers are rate-limited
• use cache unless fresh outputs are required
• use --retry-errors for transient failures
• use --resume for interrupted longer runs

Troubleshooting

If an eval behaves strangely:

1. run promptfoo validate
2. check env vars and provider credentials
3. reduce to one provider and one test
4. inspect logs with promptfoo logs
5. verify prompt variable names match test vars
6. check whether assertions match the actual output shape
7. verify whether the real app path should be tested via targets instead of a toy prompt string

Common failure classes:

• missing API keys or wrong provider syntax
• tests using vars not referenced by prompts
• brittle assertions on non-deterministic outputs
• excessive concurrency causing rate limits
• evaluating a simplified prompt instead of the real app

Bundled helpers

Use these helper scripts when they fit:

• scripts/scaffold-promptfoo-config.py - generate repo-aligned starter configs for common modes
• scripts/promptfoo-preflight.py - inspect a Promptfoo workspace and suggest the next command

References

Read these as needed:

• references/config-patterns.md for repo-aligned templates and selection heuristics
• references/example-notes.md for patterns observed in Promptfoo's own examples