Writing Promptfoo Evals

You produce maintainable promptfoo eval suites: clear test cases, deterministic assertions where possible, model-graded only when needed.

See

references/cheatsheet.md

Inputs (infer from repo context if not provided)

• What is being evaluated (prompt, agent, endpoint, RAG pipeline)?
• What are the inputs and outputs (text, JSON, multi-turn chat, tool calls)?
• What does "good" look like (acceptance criteria, failure modes)?

If context is insufficient, scaffold with TODO markers and starter tests.

Workflow

1. Find or create the eval suite

Search for existing configs:

promptfooconfig.yaml

promptfooconfig.yml

promptfoo

evals

For new suites, use this layout (unless the repo uses another convention):

evals/<suite-name>/
  promptfooconfig.yaml
  prompts/
  tests/

Always add

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

2. Write prompts

• Put prompts in

  prompts/*.txt

  (plain) or

  prompts/*.json

  (chat format)
• Reference via

  file://prompts/main.txt

• Use

  {{variable}}

  for test inputs
• If the app builds prompts dynamically, use a JS/Python provider instead of duplicating logic

3. Choose providers

Pick the simplest option that matches the real system:

openai:chat:gpt-4.1-mini

anthropic:messages:claude-sonnet-4-6

id: https

config.url

config.body

transformResponse

file://provider.py

file://provider.js

echo

Keep provider count small: 1 for regression, 2 for comparison.

For JSON output, add

response_format

config:
  temperature: 0
  response_format:
    type: json_object

4. Write tests

Use file-based tests so they scale:

tests: file://tests/*.yaml

For larger suites, use dataset-backed tests:

tests: file://tests.csv
# or
tests: file://generate_tests.py:create_tests

Every test should have:

• description

  - short, specific

• vars

  - the inputs

• assert

  - validations (when automatable)

Cover: happy paths, edge cases, known regressions, safety/refusal checks, output format compliance.

5. Add assertions

Deterministic first (fast, reliable, free):

equals

contains

icontains

regex

is-json

contains-json

starts-with

cost

latency

javascript

python

Model-graded sparingly (slow, costs money, non-deterministic):

llm-rubric

factuality

answer-relevance

context-faithfulness

Assertions support optional

weight

metric

threshold

cost

latency

For model-graded assertions, explicitly set the grader provider so grading is stable across runs:

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

tests:
  - description: 'Model-graded quality check'
    assert:
      - type: llm-rubric
        value: 'Accurate and concise'
        # Optional per-assertion override:
        # provider: anthropic:messages:claude-sonnet-4-6

Hallucination / faithfulness pattern: When checking that output is grounded in source material, include the source in the rubric so the grader can compare. Use

context-faithfulness

llm-rubric

assert:
  - type: llm-rubric
    value: |
      The summary only states facts from this source article:
      "{{article}}"
      It does not add, infer, or fabricate any claims.

JSON output pattern:

assert:
  - type: is-json
    value: # optional JSON Schema
      type: object
      required: [name, score]
  - type: javascript
    value: 'JSON.parse(output).score >= 0.8'

Transform pattern (preprocess output before assertions): When models wrap JSON in markdown fences or add preamble text, use

options.transform

options:
  transform: "output.replace(/```json\\n?|```/g, '').trim()"

Use

defaultTest

6. Validate and run

Before finishing, validate and provide run commands. Always use

--no-cache

npx promptfoo@latest validate -c <config>
npx promptfoo@latest eval -c <config> --no-cache
npx promptfoo@latest eval -c <config> -o output.json --no-cache
npx promptfoo@latest view

For CI/non-UI workflows, prefer the

-o output.json

success

score

error

If working in the promptfoo repo itself, prefer the local build:

npm run local -- validate -c <config>
npm run local -- eval -c <config> --no-cache --env-file .env

Do not run

npm run local -- view

Common mistakes

# ❌ WRONG — shell-style env vars don't work in YAML configs
apiKey: $OPENAI_API_KEY

# ✅ CORRECT — use Nunjucks syntax with quotes
apiKey: '{{env.OPENAI_API_KEY}}'

# ❌ WRONG — rubric references "the article" but grader can't see it
- type: llm-rubric
  value: 'Only contains info from the original article'

# ✅ CORRECT — inline the source so the grader can compare
- type: llm-rubric
  value: |
    Only states facts from: "{{article}}"

Output contract

When done, state:

• What the suite evaluates (1-3 bullets)
• Files created/modified (paths)
• How to run (copy-pastable commands)
• Required env vars
• TODOs left behind (only if unavoidable)