fieldtest 0.1.2

Structured AI eval practice for any project

fieldtest

The eval landscape is crowded at the execution layer and nearly empty at the practice layer.

Most eval tools assume you already know what to evaluate. You install a framework, run some metrics, see numbers. The numbers feel like quality yet they're not: they are measurements without meaning, because nobody defined what the measurements are supposed to catch before running them.

fieldtest is a tool for the layer that's missing: the reasoning that produces the evals.

The config asks you — in order — to name your use cases, define what right, good, and safe means for each, and specify how you'll test them. That sequence is the thing most teams skip, which is why they end up with evals that measure what's easy rather than what matters. The structure of the testing enforces the reasoning. With fieldtest, cannot skip to measurement without first doing the definitional work. How well you do that is up to you, but we provide the scaffolding to reason about what you are actually trying to measure.

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Install

pip install fieldtest
export ANTHROPIC_API_KEY=sk-ant-...   # for LLM judge calls

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How it works

fieldtest expects your project to have an evals/ directory with a config.yaml file. All commands default to evals/config.yaml relative to your working directory. Use --config <path> to override.

your-project/
  evals/
    config.yaml        ← fieldtest reads this
    fixtures/          ← your test inputs
    outputs/           ← your generator writes here
    results/           ← fieldtest score writes here

Run all fieldtest commands from your project root (the directory that contains evals/).

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Quickstart

1. Scaffold your eval directory

fieldtest init

This creates:

evals/
  config.yaml              ← fill this out first
  fixtures/
    golden/                ← fixtures with expected output (used for regression)
    variations/            ← fixtures without expected output
  outputs/                 ← your generator writes here (git-ignored)
  results/                 ← fieldtest score writes here
  .gitignore               ← outputs/ excluded from git

2. Fill out config.yaml

The config walks you through the reasoning in order. Here's a complete example for a resume tailoring assistant:

# evals/config.yaml
schema_version: 1

system:
  name: Resume tailoring assistant
  domain: >
    English-language resumes tailored to job descriptions.
    Input: plain-text base resume + job description.
    Output: Markdown resume tailored to the specific role.

use_cases:
  - id: tailor_resume
    description: >
      User submits a base resume and job description.
      System returns a Markdown resume tailored to the role.

    evals:

      # RIGHT — correctness evals
      # Failure → grounding or reasoning problem in your system

      - id: no_fabrication
        tag: right
        type: llm
        description: Output does not invent facts not present in the source
        pass_criteria: >
          Every company name, date, metric, and credential in the output
          can be traced to the source material. Minor rephrasing is fine.
        fail_criteria: >
          The output contains a company, date, metric, or credential that
          does not appear in the source material.

      - id: contact_preserved
        tag: right
        type: rule
        description: Name and email in output match the base resume

      # GOOD — quality evals
      # Failure → prompt engineering or format problem; iterate instructions

      - id: format_compliance
        tag: good
        type: rule
        description: Output follows required Markdown structure

      - id: bullet_quality
        tag: good
        type: llm
        description: Bullets are specific, quantified, and free of filler language
        pass_criteria: >
          Bullets begin with action verbs, are specific, include quantified
          results where the source provides data, and contain no filler phrases
          (responsible for, helped with, worked on).
        fail_criteria: >
          Bullets are vague, omit available quantification, or use filler phrases.

      # SAFE — guardrail evals
      # Failure → architectural problem; structural fix, not prompt iteration

      - id: no_preamble
        tag: safe
        type: regex
        description: Output starts with the resume, not commentary
        pattern: "^# "
        match: true

      - id: no_horizontal_rules
        tag: safe
        type: regex
        description: No --- in output (forbidden by format spec)
        pattern: "(?m)^---$"
        match: false

    fixtures:
      directory: fixtures/
      sets:
        smoke:
          # A few fixtures covering each eval type.
          # Run after any prompt change for fast signal.
          - experienced-swe__senior-swe
          - recent-grad__data-scientist
          - marketing-manager__product-manager
        regression:
          # Golden fixtures only — deterministic reference + rule + regex evals.
          # No LLM judge cost. Use this in CI on every PR.
          - experienced-swe__senior-swe
          - recent-grad__senior-swe
        full: all   # everything — run before releases
      runs: 3       # how many times to run each fixture

defaults:
  provider: anthropic
  model: claude-haiku-3-5-20251001   # judge model — NOT your system's model
  runs: 3

Sets are just named lists of fixture IDs you define. Use whatever names make sense. all is a special keyword meaning every fixture in the directory.

3. Add fixtures

A fixture is a YAML file in evals/fixtures/ describing one test case. The filename is the fixture ID.

evals/fixtures/experienced-swe__senior-swe.yaml:

id: experienced-swe__senior-swe
description: >
  Experienced SWE applying to a senior SWE role — ideal match.
  Baseline fixture; should score well across all evals.

inputs:
  resume: fixtures/resumes/experienced-swe.txt
  job:    fixtures/jobs/senior-swe.txt
  is_recent_grad: false
  expected_name:  "Alex Rivera"
  expected_email: "[e-mail]"

# The expected block makes this a "golden" fixture.
# These are deterministic string checks — no API cost.
# Base them on actual outputs you've reviewed and accepted.
expected:
  contains:
    - "[e-mail]"
    - "Stripe"
    - "## EXPERIENCE"
    - "## EDUCATION"
  not_contains:
    - "responsible for"
    - "helped with"
    - "---"

A fixture without an expected block is a variation fixture — only rule, regex, and LLM evals run on it. Use variations when you don't have reviewed expected output yet. Add them to golden/ once you've reviewed outputs and written the expected block.

The inputs block is yours to define. Whatever your generator needs — file paths, flags, metadata — put it here. Your generator reads inputs directly.

4. Write your generator

The generator is a script you write (~30 lines). It calls your system and writes outputs to evals/outputs/[fixture-id]/run-N.txt. fieldtest only reads those files — it never calls your system directly.

evals/generate.py:

import os
import pathlib
import sys
import yaml
import anthropic

SYSTEM_PROMPT = "You are a resume tailoring assistant..."
MODEL = "claude-sonnet-4-20250514"

def tailor_resume(resume_text, job_text):
    client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])
    message = client.messages.create(
        model=MODEL,
        max_tokens=4096,
        system=SYSTEM_PROMPT,
        messages=[{"role": "user", "content": job_text}],
    )
    return message.content[0].text

def main():
    config    = yaml.safe_load(pathlib.Path("evals/config.yaml").read_text())
    set_name  = sys.argv[1] if len(sys.argv) > 1 else "full"
    base_dir  = pathlib.Path("evals")
    runs      = config["defaults"]["runs"]

    fixture_ids = config["use_cases"][0]["fixtures"]["sets"][set_name]
    if fixture_ids == "all":
        fixture_ids = [p.stem for p in sorted((base_dir / "fixtures").rglob("*.yaml"))]

    for fixture_id in fixture_ids:
        fixture = yaml.safe_load((base_dir / "fixtures" / f"{fixture_id}.yaml").read_text())
        inputs  = fixture["inputs"]

        resume_text = (base_dir / inputs["resume"]).read_text()
        job_text    = (base_dir / inputs["job"]).read_text()

        out_dir = base_dir / "outputs" / fixture_id
        out_dir.mkdir(parents=True, exist_ok=True)

        for run in range(1, runs + 1):
            print(f"  {fixture_id} run {run}/{runs}...", end=" ", flush=True)
            output = tailor_resume(resume_text, job_text)
            (out_dir / f"run-{run}.txt").write_text(output)
            print("✓")

if __name__ == "__main__":
    main()

Run it for a specific set:

python3 evals/generate.py smoke    # run only the smoke set
python3 evals/generate.py full     # run everything

5. Score

fieldtest score

Output:

scoring tailor_resume: 3 fixtures × 3 runs = 9 evaluations per eval
✓ results written to evals/results/2026-03-24T14-30-00-a3f9

Four files are written to evals/results/:

2026-03-24T14-30-00-a3f9-data.json     full result data, machine-readable
2026-03-24T14-30-00-a3f9-data.csv      flat rows, one per fixture × eval × run
2026-03-24T14-30-00-a3f9-report.md     human report
2026-03-24T14-30-00-a3f9-report.csv    spreadsheet report

The -report.md looks like:

# Eval Report
2026-03-24 14:30 | set: full | 3 fixtures × 3 runs = 9 evaluations per eval

---

## tailor_resume

### Tag Health
| tag   | pass rate | passed / total |
|-------|-----------|----------------|
| RIGHT | 100%      | 18 / 18        |
| GOOD  | 91%       | 33 / 36        |
| SAFE  | 100%      | 54 / 54        |

### RIGHT
| eval              | failure rate | errors | vs prior |
|-------------------|-------------|--------|---------|
| no_fabrication    | 0%          | 0      | ↔        |
| contact_preserved | 0%          | 0      | ↔        |

### GOOD
| eval              | failure rate | errors | vs prior |
|-------------------|-------------|--------|---------|
| format_compliance | 0%          | 0      | ↔        |
| bullet_quality    | 9%          | 0      | +3%      |

### SAFE
| eval                | failure rate | errors | vs prior |
|---------------------|-------------|--------|---------|
| no_preamble         | 0%          | 0      | ↔        |
| no_horizontal_rules | 0%          | 0      | ↔        |

### Fixture × Eval Matrix
| fixture                    | no_fabrication | contact_preserved | format_compliance | bullet_quality | no_preamble | no_horizontal_rules |
| ---                        | ---            | ---               | ---               | ---            | ---         | ---                 |
| experienced-swe__senior-swe | 3/3           | 3/3               | 3/3               | 3/3            | 3/3         | 3/3                 |
| recent-grad__data-scientist | 3/3           | 3/3               | 3/3               | 2/3            | 3/3         | 3/3                 |
| marketing-manager__pm       | 3/3           | 3/3               | 3/3               | 2/3            | 3/3         | 3/3                 |

### Failure Details

**bullet_quality**
- `recent-grad__data-scientist` run 2: Bullets omit available quantification from source
- `marketing-manager__pm` run 1: "Responsible for managing" — filler phrase present

The tool reports distributions. You decide what's a regression. bullet_quality failing on 2 of 9 runs might be acceptable or might need a prompt fix — you know your system's risk tolerance; the tool doesn't.

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CLI Reference

fieldtest validate

Check that your config is valid before running anything.

fieldtest validate
fieldtest validate --config path/to/config.yaml

✓ config valid — 1 use case, 6 evals, 8 fixtures

On error:

Error: eval 'no_fabrication' (type: llm) missing required field: pass_criteria

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fieldtest score

Score all fixtures in the full set (the default).

fieldtest score
fieldtest score --set smoke        # fast subset
fieldtest score --set regression   # golden fixtures only
fieldtest score --config path/to/config.yaml

Sets are defined in your config under fixtures.sets. There's nothing special about the names smoke, regression, or full — use whatever names fit your workflow. The only special value is all, which means every fixture in the directory.

fixtures:
  sets:
    smoke:      [fixture-a, fixture-b]   # named list of fixture IDs
    regression: golden/*                 # all fixtures in a subdirectory
    full:       all                      # every fixture in fixtures/

Golden fixtures are just fixtures with an expected block. The regression set conventionally contains these — but "golden" and "regression" are just conventions, not enforced by the tool. What makes a fixture golden is whether it has expected.contains or expected.not_contains entries, not which set it's in.

---

fieldtest score --allow-partial

By default, fieldtest score exits with an error if any expected output file is missing. Use --allow-partial to skip missing outputs and continue scoring what exists.

fieldtest score --allow-partial

⚠ partial results: recent-grad__data-scientist run 2, recent-grad__data-scientist run 3 not found — excluded from rates
scoring tailor_resume: 2 fixtures × 3 runs (PARTIAL — 2 outputs missing, skipped)
✓ results written to evals/results/2026-03-24T14-30-00-a3f9

Skipped runs are excluded from failure rates — they don't count as passes or failures. The report header flags the run as partial so you know the rates are based on incomplete data. All available outputs are still scored normally.

Use this when you're iterating on evals and don't have complete generated outputs yet, or when a generator run partially failed.

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fieldtest score --concurrency 1

By default fieldtest dispatches judge calls in parallel (5 threads) and prints the full report only at the end. --concurrency 1 runs judges sequentially and prints each result as it completes — useful when debugging a judge error.

fieldtest score --concurrency 1

  no_fabrication                 experienced-swe__senior-swe  run 1  ✓ pass
  no_fabrication                 experienced-swe__senior-swe  run 2  ✓ pass
  no_fabrication                 experienced-swe__senior-swe  run 3  ✓ pass
  contact_preserved              experienced-swe__senior-swe  run 1  ✓ pass
  bullet_quality                 recent-grad__data-scientist  run 1  ✗ fail
  bullet_quality                 recent-grad__data-scientist  run 2  ✓ pass
  no_fabrication                 marketing-manager__pm        run 1  ⚠ error
  ...

When a judge is erroring (API failure, malformed response), --concurrency 1 shows you exactly which fixture and run is triggering it. With parallel execution the errors surface only in the final report, mixed with everything else.

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fieldtest history

List all past runs, newest first, with tag-level failure rates.

fieldtest history

RUN ID                      TIMESTAMP           SET           FIXTURES    RIGHT     GOOD      SAFE
2026-03-24T14-30-00-a3f9    2026-03-24 14:30    full          11          0%        9%        0%
2026-03-24T11-31-00-da96    2026-03-24 11:31    full          11          0%        18%       0%
2026-03-23T18-52-00-79fb    2026-03-23 18:52    smoke         6           0%        12%       0%

The failure rates shown are averages across all evals with that tag. Use this to spot when a set of changes improved or hurt a whole category. Open the -report.md for the specific run to see which evals moved.

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fieldtest diff

Compare two runs. Default: most recent vs prior (same set).

fieldtest diff                                           # most recent vs prior
fieldtest diff 2026-03-24T14-30-00-a3f9                 # specific run vs its prior
fieldtest diff 2026-03-24T14-30-00-a3f9 \
  --baseline 2026-03-23T18-52-00-79fb                   # explicit comparison

Comparing: 2026-03-24T14-30-00-a3f9
Baseline:  2026-03-23T18-52-00-79fb

Increased:
  bullet_quality: 0.180 → 0.090 (+0.090)

Decreased:
  education_placement: 0.240 → 0.180 (-0.060)

Unchanged: no_fabrication, contact_preserved, format_compliance, no_preamble, no_horizontal_rules

Deltas use neutral language — "increased" means the failure rate went up, "decreased" means it went down. You decide if a change is a regression. A decrease in education_placement failure rate after a prompt fix is expected. An increase in no_fabrication is always worth investigating.

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fieldtest clean

Remove accumulated run artifacts.

# Interactive — shows what would be removed, asks to confirm
fieldtest clean

# Clear outputs/ (your generator's output files)
fieldtest clean --outputs

# Prune old results, keeping the 10 most recent
fieldtest clean --results --keep 10

# Both
fieldtest clean --outputs --results --keep 5

Interactive mode:

Would remove:
  outputs/: 33 run files
  results/: 8 old result sets (keeping 20)
Proceed? [y/N]:

Only what's listed in the prompt gets removed. If only results need pruning, outputs are untouched.

--keep defaults to 20. Each result set is 4 files (-data.json, -data.csv, -report.md, -report.csv); all four are removed together when pruning.

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fieldtest init

Scaffold the eval directory structure in your project. Safe to run in an existing project — won't overwrite files unless you pass --force.

fieldtest init              # creates evals/ in current directory
fieldtest init --dir ci/evals   # custom location
fieldtest init --force      # overwrite existing files

✓ Scaffolded eval structure at evals/
  evals/config.yaml       — fill this out first
  evals/fixtures/golden/  — fixtures with expected outputs
  evals/fixtures/variations/ — fixtures without expected outputs
  evals/.gitignore        — outputs/ excluded from git

Next steps:
  1. Edit evals/config.yaml
  2. Add fixtures to evals/fixtures/
  3. Run your system → write outputs to evals/outputs/
  4. fieldtest score

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Right / Good / Safe

Every eval requires a tag. The tag is the diagnostic path when something fails.

tag	what it means	failure →
right	correctness — did the system do the correct thing?	grounding, retrieval, or reasoning fix
good	quality — did the system do it well?	prompt engineering or format fix
safe	guardrails — did the system violate a hard constraint?	architectural fix, not prompt iteration

A single quality score hides which category failed. right and safe failures have completely different fixes — one is a reasoning problem, one is a structural problem. Tagging forces you to classify before you measure.

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Eval types

type	when to use	example
rule	deterministic Python logic; can read fixture inputs	contact info check, section ordering
regex	pattern matching; match: true = must match, match: false = must not match	forbidden strings, required format
llm	semantic judgment that requires reading the output	fabrication, quality, keyword alignment
reference	compare against expected block in fixture file	golden output regression check

Writing rules:

# evals/rules.py
from fieldtest import rule

@rule("contact_preserved")
def check_contact(output: str, inputs: dict) -> dict:
    name  = inputs.get("expected_name", "")
    email = inputs.get("expected_email", "")
    header = "\n".join(output.splitlines()[:3])
    if name and name not in header:
        return {"passed": False, "detail": f"'{name}' not in first 3 lines"}
    if email and email not in header:
        return {"passed": False, "detail": f"'{email}' not in first 3 lines"}
    return {"passed": True, "detail": "name and email present"}

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Two LLMs, two purposes

Your generator calls your system. fieldtest score calls its own judge LLM. Completely separate — different models, different credentials, different purposes.

YOUR SYSTEM (generator)              JUDGE (fieldtest score)
─────────────────────────────────    ──────────────────────────────────
calls your model or pipeline         calls a judge LLM to score outputs
configured by: your generator code   configured by: defaults.model in config.yaml
auth: your credentials               auth: ANTHROPIC_API_KEY in environment

defaults.model in config is the judge model. Set it independently of whatever your system uses.

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Results files

Four files per run, named [run-id]-data.* or [run-id]-report.*:

file	what it is
[run-id]-data.json	Full result data — rows, summary, delta. Machine-readable, CI-parseable.
[run-id]-data.csv	Flat rows, one per fixture × eval × run. Analyst-ready.
[run-id]-report.md	Human report — tag health, per-eval tables, fixture × eval matrix, failure details.
[run-id]-report.csv	Spreadsheet report — same three views, designed to open in Excel or Numbers.

CI gating: fieldtest score exits 0 on success, 1 on error. It does not exit non-zero on high failure rates — the tool measures; you judge. To gate CI on specific failure rates, parse the -data.json:

python3 -c "
import json, glob, sys
f = sorted(glob.glob('evals/results/*-data.json'))[-1]
rows = json.load(open(f))['rows']
failures = [r for r in rows if r['eval_id'] == 'no_fabrication' and r.get('passed') is False]
if failures:
    print(f'no_fabrication failed on {len(failures)} runs')
    sys.exit(1)
"

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Examples and patterns

• examples/generate_anthropic.py — complete generator calling Claude directly
• examples/generate_openai.py — complete generator calling OpenAI
• examples/generate_subprocess.py — complete generator calling any CLI tool
• examples/generate-patterns.md — sets, CI integration, scheduling, multiple generators, production traffic sampling
• examples/eval-patterns.md — eval design cookbook: refusals, format compliance, forbidden content, conditional behavior, classification, and more

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The practice is the point. The tool makes the practice tractable.