AI Calibrator

Turn your knowledge and standards into a tested, reliable AI — without writing prompts, code, or datasets. You bring your materials and answer questions; the tool builds, tests, and proves an AI that behaves the way you want.

Think of it like onboarding a brilliant new hire: it interviews you about how you want the job done, writes the playbook, then quizzes itself against that playbook until it reliably passes.

How it works

1. State the goal — what should this AI do?
2. Upload your materials — docs, examples, policies. The tool indexes them and finds the gaps they don't cover.
3. Answer a short interview — only about the gaps; it drafts likely answers for you to approve or correct.
4. It compiles a behavior spec → system prompt + knowledge lookup (RAG) + an eval rubric + test cases.
5. It tests and scores the AI against your standards, fixes failures, and loops until it passes.
6. You get a finished, runnable AI plus the saved spec and tests.

Guided mode (default) does all of this with configuration — runs on any machine. Advanced mode (opt-in, technical users) adds a fine-tuning toolchain on top, gated on actually beating the configured baseline.

Bring-your-own-key: the engine defaults to Claude via your own API key, but works equally with OpenAI (<model>@openai, incl. OpenAI-compatible endpoints) or a local Ollama model (no key / offline). The app runs on your machine and no secrets ship in this repo.

Status

Alpha (v0.0.2) — but the whole pipeline is built and tested: the Guided loop (init → ingest → interview → compile → eval → export, CLI + local web UI), a deep verification surface (spec lint, deterministic checks, LLM-judge with self-consistency + human judge calibration, coverage, red-team, drift, golden snapshots, weighted scoring — composed into one calibrate ci gate), and the Advanced tier (fine-tuning + Engine-Trainer with prove-it gates). calibrate compare runs your suite against the same model without the calibration, so "the calibrated AI is better" is a measured delta on your own tests, not a claim. The test suite runs engine-free with fakes; the pipeline is also verified end-to-end against a real local model via Ollama.

Build plan: docs/BUILD-PLAN.md · Architecture: docs/ARCHITECTURE.md · Contributing: CONTRIBUTING.md

Quickstart

python3 -m venv .venv && source .venv/bin/activate   # required on stock macOS / modern Debian (PEP 668)
pip install 'ai-calibrator[cloud]'       # or '[all]' for every engine + the web UI (note: [all] pulls a multi-GB ML stack via [rag])
export ANTHROPIC_API_KEY=<your-key>      # your own key; nothing is stored in the repo
calibrate --help
calibrate init my-support-ai --goal "Answer customer product questions in our voice."
calibrate status my-support-ai

The engine defaults to Claude (cloud, bring-your-own key). To use OpenAI, set OPENAI_API_KEY and bind roles to <model>@openai. To run locally with no key, install Ollama, pull a model (e.g. ollama pull qwen2.5:7b), and point every role at it:

calibrate engines my-support-ai --all qwen2.5:7b@ollama

--all points the judge at that model too, so it grades its own answers. That works, and it is often the only option locally — but the pass rate is then one model's opinion of itself, and the failures it cannot see are exactly the ones it cannot report. calibrate lint says so, and there are two ways to earn a stronger number: bind the judge to a different model (calibrate engines my-support-ai judge <model@provider>), or make the criteria that matter deterministic with calibrate add-check, which does not consult a judge at all.

📖 Full walkthrough: docs/USAGE.md — engine setup for Claude / OpenAI / local, the step-by-step workflow, and current build status. License: MIT.